2019

  • [DOI] E. Ewing, L. Kular, S. J. Fernandes, N. Karathanasis, V. Lagani, S. Ruhrmann, I. Tsamardinos, J. Tegner, F. Piehl, D. Gomez-Cabrero, and M. Jagodic, “Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression,” EBioMedicine, vol. 43, pp. 411-423, 2019.
    [Summary]

  • [DOI] M. S. Loos, R. Ramakrishnan, W. Vranken, A. Tsirigotaki, E. Tsare, V. Zorzini, J. D. Geyter, B. Yuan, I. Tsamardinos, M. Klappa, J. Schymkowitz, F. Rousseau, S. Karamanou, and A. Economou, “Structural Basis of the Subcellular Topology Landscape of Escherichia coli,” Frontiers in Microbiology, vol. 10, 2019.
    [Summary]

  • [DOI] E. Ewing, L. Kular, S. J. Fernandes, N. Karathanasis, V. Lagani, S. Ruhrmann, I. Tsamardinos, J. Tegner, F. Piehl, D. Gomez-Cabrero, and M. Jagodic, “Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression,” EBioMedicine, 2019.
    [Summary]

    Abstract Background Multiple Sclerosis (MS) is a chronic inflammatory disease and a leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes and environment to control cellular functions on a molecular level, may provide insights into MS pathogenesis. Methods We measured DNA methylation in CD4+ T cells (n = 31), CD8+ T cells (n = 28), CD14+ monocytes (n = 35) and CD19+ B cells (n = 27) from relapsing-remitting (RRMS), secondary progressive (SPMS) patients and healthy controls (HC) using Infinium HumanMethylation450 arrays. Monocyte (n = 25) and whole blood (n = 275) cohorts were used for validations. Findings B cells from MS patients displayed most significant differentially methylated positions (DMPs), followed by monocytes, while only few DMPs were detected in T cells. We implemented a non-parametric combination framework (omicsNPC) to increase discovery power by combining evidence from all four cell types. Identified shared DMPs co-localized at MS risk loci and clustered into distinct groups. Functional exploration of changes discriminating RRMS and SPMS from HC implicated lymphocyte signaling, T cell activation and migration. SPMS-specific changes, on the other hand, implicated myeloid cell functions and metabolism. Interestingly, neuronal and neurodegenerative genes and pathways were also specifically enriched in the SPMS cluster. Interpretation We utilized a statistical framework (omicsNPC) that combines multiple layers of evidence to identify DNA methylation changes that provide new insights into MS pathogenesis in general, and disease progression, in particular. Fund This work was supported by the Swedish Research Council, Stockholm County Council, AstraZeneca, European Research Council, Karolinska Institutet and Margaretha af Ugglas Foundation.

  • [DOI] I. Ferreirós-Vidal, T. Carroll, T. Zhang, V. Lagani, R. N. Ramirez, E. Ing-Simmons, A. Garcia, L. Cooper, Z. Liang, G. Papoutsoglou, G. Dharmalingam, Y. Guo, S. Tarazona, S. J. Fernandes, P. Noori, G. Silberberg, A. G. Fisher, I. Tsamardinos, A. Mortazavi, B. Lenhard, A. Conesa, J. Tegner, M. Merkenschlager, and D. Gomez-Cabrero, “Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation,” PLOS Biology, vol. 17, iss. 4, pp. 1-28, 2019.
    [Summary]

    Author summary The human body is made from billions of cells comprizing many specialized cell types. All of these cells ultimately come from a single fertilized oocyte in a process that has two key features: proliferation, which expands cell numbers, and differentiation, which diversifies cell types. Here, we have examined the transition from proliferation to differentiation using B lymphocytes as an example. We find that the transition from proliferation to differentiation involves changes in the expression of genes, which can be categorized into cell-type–specific genes and broadly expressed “housekeeping” genes. The expression of many housekeeping genes is controlled by the gene regulatory factor Myc, whereas the expression of many B lymphocyte–specific genes is controlled by the Ikaros family of gene regulatory proteins. Myc is repressed by Ikaros, which means that changes in housekeeping and tissue-specific gene expression are coordinated during the transition from proliferation to differentiation.

  • [DOI] Y. Pantazis and I. Tsamardinos, “A unified approach for sparse dynamical system inference from temporal measurements,” , 2019.
    [Summary]

    Temporal variations in biological systems and more generally in natural sciences are typically modeled as a set of ordinary, partial or stochastic differential or difference equations. Algorithms for learning the structure and the parameters of a dynamical system are distinguished based on whether time is discrete or continuous, observations are time-series or time-course and whether the system is deterministic or stochastic, however, there is no approach able to handle the various types of dynamical systems simultaneously.In this paper, we present a unified approach to infer both the structure and the parameters of non-linear dynamical systems of any type under the restriction of being linear with respect to the unknown parameters. Our approach, which is named Unified Sparse Dynamics Learning (USDL), constitutes of two steps. First, an atemporal system of equations is derived through the application of the weak formulation. Then, assuming a sparse representation for the dynamical system, we show that the inference problem can be expressed as a sparse signal recovery problem, allowing the application of an extensive body of algorithms and theoretical results. Results on simulated data demonstrate the efficacy and superiority of the USDL algorithm under multiple interventions and/or stochasticity. Additionally, USDL’s accuracy significantly correlates with theoretical metrics such as the exact recovery coefficient. On real single-cell data, the proposed approach is able to induce high-confidence subgraphs of the signaling pathway.Source code is available at Bioinformatics online. USDL algorithm has been also integrated in SCENERY (http://scenery.csd.uoc.gr/); an online tool for single-cell mass cytometry analytics.Supplementary data are available at Bioinformatics online.

  • [PDF] “Forward-Backward Selection with Early Dropping,” Journal of Machine Learning Research, vol. 20, pp. 1-39, 2019.
    [Summary]

    Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive performance. The idea is to temporarily discard the variables that are conditionally independent with the outcome given the selected variable set. Depending on how those variables are reconsidered and reintroduced, this heuristic gives rise to a family of algorithms with increasingly stronger theoretical guarantees. In distributions that can be faithfully represented by Bayesian networks or maximal ancestral graphs, members of this algorithmic family are able to correctly identify the Markov blanket in the sample limit. In experiments we show that the proposed heuristic increases computational efficiency by about 1-2 orders of magnitude, while selecting fewer or the same number of variables and retaining predictive performance. Furthermore, we show that the proposed algorithm and feature selection with LASSO perform similarly when restricted to select the same number of variables, making the proposed algorithm an attractive alternative for problems where no (efficient) algorithm for LASSO exists

2018

  • [DOI] K. Tsirlis, V. Lagani, S. Triantafillou, and I. Tsamardinos, “On scoring Maximal Ancestral Graphs with the Max\textendashMin Hill Climbing algorithm,” International Journal of Approximate Reasoning, vol. 102, pp. 74-85, 2018.
    [Summary]

    We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal-network reconstruction in presence of latent confounders.

  • [DOI] M. Tsagris, “Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation,” Applied Artificial Intelligence , vol. 33, iss. 2, pp. 101-123, 2018.
    [Summary]

    PC is a prototypical constraint-based algorithm for learning Bayesian networks, a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg, was developed to make the skeleton phase order independent. In return, it has notably increased execution time. In this paper, we clarify that the PC algorithm the skeleton phase of PC is indeed order independent. The modification we propose outperforms pcalg’s variant of the PC in terms of returning correct networks of better quality as is less prone to errors and in some cases it is a lot more computationally cheaper. In addition, we show that pcalg’s variant does not return valid acyclic graphs.

  • [DOI] I. Tsamardinos, E. Greasidou, and G. Borboudakis, “Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation,” Machine Learning, vol. 107, iss. 12, pp. 1895-1922, 2018.
    [Summary]

    Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically biased. We present an efficient bootstrap method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV). BBC-CV’s main idea is to bootstrap the whole process of selecting the best-performing configuration on the out-of-sample predictions of each configuration, without additional training of models. In comparison to the alternatives, namely the nested cross-validation (Varma and Simon in BMC Bioinform 7(1):91, 2006) and a method by Tibshirani and Tibshirani (Ann Appl Stat 822–829, 2009), BBC-CV is computationally more efficient, has smaller variance and bias, and is applicable to any metric of performance (accuracy, AUC, concordance index, mean squared error). Subsequently, we employ again the idea of bootstrapping the out-of-sample predictions to speed up the CV process. Specifically, using a bootstrap-based statistical criterion we stop training of models on new folds of inferior (with high probability) configurations. We name the method Bootstrap Bias Corrected with Dropping CV (BBCD-CV) that is both efficient and provides accurate performance estimates.

  • [DOI] I. Tsamardinos, G. Borboudakis, P. Katsogridakis, P. Pratikakis, and V. Christophides, “A greedy feature selection algorithm for Big Data of high dimensionality,” Machine Learning, 2018.
    [Summary]

    We present the Parallel, Forward–Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis techniques, PFBP relies only on computations local to a partition while minimizing communication costs, thus massively parallelizing computations. Similar techniques for combining local computations are also employed to create the final predictive model. PFBP employs asymptotically sound heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores. An extensive comparative evaluation also demonstrates the effectiveness of PFBP against other algorithms in its class. The heuristics presented are general and could potentially be employed to other greedy-type of FS algorithms. An application on simulated Single Nucleotide Polymorphism (SNP) data with 500K samples is provided as a use case.

  • [DOI] M. Adamou, G. Antoniou, E. Greasidou, V. Lagani, P. Charonyktakis, I. Tsamardinos, and M. Doyle, “Toward Automatic Risk Assessment to Support Suicide Prevention,” Crisis, pp. 1-8, 2018.
    [Summary]

    Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims: We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method: The analysis used data of patients who died by suicide in the period 2013–2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations: Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion: The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

  • [DOI] K. Lakiotaki, N. Vorniotakis, M. Tsagris, G. Georgakopoulos, and I. Tsamardinos, “BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology,” Database, iss. bay011, 2018.
    [Summary]

    Biotechnology revolution generates a plethora of omics data with an exponential growth pace. Therefore, biological data mining demands automatic, ‘high quality’ curation efforts to organize biomedical knowledge into online databases. BioDataome is a database of uniformly preprocessed and disease-annotated omics data with the aim to promote and accelerate the reuse of public data. We followed the same preprocessing pipeline for each biological mart (microarray gene expression, RNA-Seq gene expression and DNA methylation) to produce ready for downstream analysis datasets and automatically annotated them with disease-ontology terms. We also designate datasets that share common samples and automatically discover control samples in case-control studies. Currently, BioDataome includes ∼5600 datasets, ∼260 000 samples spanning ∼500 diseases and can be easily used in large-scale massive experiments and meta-analysis. All datasets are publicly available for querying and downloading via BioDataome web application. We demonstrate BioDataome’s utility by presenting exploratory data analysis examples. We have also developed BioDataome R package found in: https://github.com/mensxmachina/BioDataome/. Database URL: http://dataome.mensxmachina.org/

  • [DOI] M. Markaki, I. Tsamardinos, A. Langhammer, V. Lagani, K. Hveem, and O. D. Røe, “A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study.,” EBioMedicine, 2018.
    [Summary]

    Lung cancer causes >1·6 million deaths annually, with early diagnosis being paramount to effective treatment. Here we present a validated risk assessment model for lung cancer screening. The prospective HUNT2 population study in Norway examined 65,237 people aged >20years in 1995-97. After a median of 15·2years, 583 lung cancer cases had been diagnosed; 552 (94·7%) ever-smokers and 31 (5·3%) never-smokers. We performed multivariable analyses of 36 candidate risk predictors, using multiple imputation of missing data and backwards feature selection with Cox regression. The resulting model was validated in an independent Norwegian prospective dataset of 45,341 ever-smokers, in which 675 lung cancers had been diagnosed after a median follow-up of 11·6years. Our final HUNT Lung Cancer Model included age, pack-years, smoking intensity, years since smoking cessation, body mass index, daily cough, and hours of daily indoors exposure to smoke. External validation showed a 0·879 concordance index (95% CI 0·866-0·891) with an area under the curve of 0·87 (95% CI 0·85-0·89) within 6years. Only 22% of ever-smokers would need screening to identify 81·85% of all lung cancers within 6years. Our model of seven variables is simple, accurate, and useful for screening selection.

  • M. Panagopoulou, M. Karaglani, I. Balgkouranidou, V. Vasilakakis, E. Biziota, T. Koukaki, E. Karamitrousis, E. Nena, I. Tsamardinos, G. Kolios, E. Lianidou, S. Kakolyris, and E. Chatzaki, “Circulating cell free DNA in Breast cancer: size profiling, levels and methylation patterns lead to prognostic and predictive classifiers,” (to appear) Oncogene , 2018.
    [Summary]

    Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups. The methylation status of a panel of cancer-related genes chosen based on previous expression and epigenetic data (KLK10, SOX17, WNT5A, MSH2, GATA3) was assessed by quantitative methylation-specific PCR. All but the GATA3 gene was more frequently methylated in all the patient groups than in healthy individuals (all p < 0.05). The methylation of WNT5A was statistically significantly correlated to greater tumor size and poor prognosis characteristics and in advanced stage disease with shorter OS. In the metastatic group, also SOX17 methylation was significantly correlated to the incidence of death, shorter PFS, and OS. KLK10 methylation was significantly correlated to unfavorable clinicopathological characteristics and relapse, whereas in the adjuvant group to shorter DFI. Methylation of at least 3 or 4 genes was significantly correlated to shorter OS and no pharmacotherapy response, respectively. Classification analysis by a fully automated, machine learning software produced a single-parametric linear model using ccfDNA plasma concentration values, with great discriminating power to predict response to chemotherapy (AUC 0.803, 95% CI [0.606, 1.000]) in the metastatic group. Two more multi-parametric signatures were produced for the metastatic group, predicting survival and disease outcome. Finally, a multiple logistic regression model was constructed, discriminating between patient groups and healthy individuals. Overall, ccfDNA emerged as a highly potent predictive classifier in metastatic breast cancer. Upon prospective clinical evaluation, all the signatures produced could aid accurate prognosis.

  • [DOI] M. Tsagris, V. Lagani, and I. Tsamardinos, ” Feature selection for high-dimensional temporal data,” BMC Bioinformatics, iss. 1, 2018.
    [Summary]

    Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work, we extend established constrained-based, feature-selection methods to high-dimensional “omics” temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables. The algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics. The use of this algorithm is suggested for variable selection with high-dimensional temporal data.

  • [DOI] M. Tsagris, G. Borboudakis, V. Lagani, and I. Tsamardinos, “Constraint-based causal discovery with mixed data,” International Journal of Data Science and Analytics, 2018.
    [Summary]

    We address the problem of constraint-based causal discovery with mixed data types, such as (but not limited to) continuous, binary, multinomial and or-dinal variables. We use likelihood-ratio tests based on appropriate regression models, and show how to derive symmetric conditional independence tests. Such tests can then be directly used by existing constraint-based methods with mixed data, such as the PC and FCI algorithms for learning Bayesian networks and maximal ancestral graphs respectively. In experiments on simu-lated Bayesian networks, we employ the PC algorithm with different conditional independence tests for mixed data, and show that the proposed approach outperforms alternatives in terms of learning accuracy.

  • [DOI] M. Adamou, G. Antoniou, E. Greassidou, V. Lagani, P. Charonyktakis, and I. Tsamardinos, “Mining Free-Text Medical Notes for Suicide Risk Assessment.” 2018.
    [Summary]

    Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

2017

  • [DOI] V. Lagani, G. Athineou, A. Farcomeni, M. Tsagris, and I. Tsamardinos, “Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets,” Journal of Statistical Software, vol. 80, iss. 7, 2017.
    [Summary]

    The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constraint-based learning of Bayesian networks. Most of the currently available feature selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. In that respect the SES algorithm subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. The SES algorithm is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning ‘mind from the machine’ in Latin. The MXM implementation of SES handles several data analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of predictive accuracy and that multiple, equally predictive signatures are actually present in real world data.

  • [DOI] G. Orfanoudaki, M. Markaki, K. Chatzi, I. Tsamardinos, and A. Economou, “MatureP: prediction of secreted proteins with exclusive information from their mature regions,” Scientific Reports, vol. 7, iss. 1, p. 3263–, 2017.
    [Summary]

    More than a third of the cellular proteome is non-cytoplasmic. Most secretory proteins use the Sec system for export and are targeted to membranes using signal peptides and mature domains. To specifically analyze bacterial mature domain features, we developed MatureP, a classifier that predicts secretory sequences through features exclusively computed from their mature domains. MatureP was trained using Just Add Data Bio, an automated machine learning tool. Mature domains are predicted efficiently with ~92% success, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC). Predictions were validated using experimental datasets of mutated secretory proteins. The features selected by MatureP reveal prominent differences in amino acid content between secreted and cytoplasmic proteins. Amino-terminal mature domain sequences have enhanced disorder, more hydroxyl and polar residues and less hydrophobics. Cytoplasmic proteins have prominent amino-terminal hydrophobic stretches and charged regions downstream. Presumably, secretory mature domains comprise a distinct protein class. They balance properties that promote the necessary flexibility required for the maintenance of non-folded states during targeting and secretion with the ability of post-secretion folding. These findings provide novel insight in protein trafficking, sorting and folding mechanisms and may benefit protein secretion biotechnology.

  • [DOI] G. Papoutsoglou, G. Athineou, V. Lagani, I. Xanthopoulos, A. Schmidt, S. éliás, J. Tegnér, and I. Tsamardinos, “SCENERY: a web application for (causal) network reconstruction from cytometry data,” Nucleic Acids Research, vol. 37, p. D412–D416, 2017.
    [Summary]

    Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.

  • [DOI] K. Siomos, E. Papadaki, I. Tsamardinos, K. Kerkentzes, M. Koygioylis, and C. Trakatelli, “Prothrombotic and Endothelial Inflammatory Markers in Greek Patients with Type 2 Diabetes Compared to Non-Diabetics,” Endocrinology & Metabolic Syndrome, iss. 1, 2017.
    [Summary]

    Objective: To evaluate specific factors of coagulation and endothelial inflammatory markers namely, thrombomodulin, soluble receptor of the protein C (sEPCR), factor VIII, plasminogen activator inhibitor 1, Von Willebrandt factor, fibrinogen, fibrinogen dimers (d-dimers), high sensitivity C-reactive protein and homocysteine in a subset of Greek subjects with and without Type 2 (T2) Diabetes. Design: 84 subjects, of which 44 patients with T2 diabetes, were included in the randomized comparative prospective cross sectional study. The subjects were split into a Τ2 diabetics group and a group of healthy controls of similar age, anthropometric profiles and similar gender distribution. Results: A total of 47 variables and biomarkers together with indicators for metabolic profiles, clinical history, as well as detailed anthropometric profiles and traditional risk factors, were evaluated. Dipeptidyl peptidase-4 (DPP4), Insulin, use of Sulfonylurea, high HBA1c and glucose levels, were clearly statistically differentiated in the two groups, while no other biomarkers including the new potential indicators were found to be different. High values of thrombomodulin and homocysteine were correlated with a rise in creatinine and thus seem to affect renal function in the diabetic patients group while in the non-diabetics group the correlations are different with sEPCR having a relative strong negative correlation in renal function as measured with The Modification of Diet in Renal Disease, in agreement with the latest international findings. Conclusions: The presence of T2 diabetes in conjunction with age clearly correlates with problems in renal function, thrombomodulin and homocysteine could serve as indicators for renal damage in diabetics but not in healthy individuals. sEPCR on the other hand could be a potential generic indicator for renal damage. Thrombomodulin and sEPCR as prothombotic agents, did not show any indication that they can be utilised as markers for the prevention and/or treatment of thrombotic complications in diabetic patients.

  • [DOI] S. Triantafillou, V. Lagani, C. Heinze-Deml, A. Schmidt, J. Tegner, and I. Tsamardinos, “Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells,” Triantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, Tsamardinos I. Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports. 2017;7:12724. doi:10., 2017.
    [Summary]

    Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

2016

  • S. Triantafillou and I. Tsamardinos, “Score based vs constraint based causal learning in the presence of confounders.” 2016.
    [Summary]

    We compare score-based and constraint-based learning in the presence of latent confounders. We use a greedy search strategy to identify the best fitting maximal ancestral graph (MAG) from continuous data, under the assumption of multivariate normality. Scoring maximal ancestral graphs is based on (a) residual iterative conditional fitting [Drton et al., 2009] for obtaining maximum likelihood estimates for the parameters of a given MAG and (b) factorization and score decomposition results for mixed causal graphs [Richardson, 2009, Nowzohour et al., 2015]. We compare the score-based approach in simulated settings with two standard constraintbased algorithms: FCI and conservative FCI. Results show a promising performance of the greedy search algorithm

  • A. Roumpelaki, G. Borboudakis, S. Triantafillou, and I. Tsamardinos, “Marginal causal consistency in constraint-based causal learning.” 2016.
    [Summary]

  • O. D. Roe, M. Markaki, R. Mjelle, Pål. Sætrom, I. Tsamardinos, and V. Lagani, “Serum microRNAs/enriched pathways in lung cancer 1-4 years before diagnosis: A pilot study from the HUNT Biobank, Norway..” 2016.
    [Summary]

  • [DOI] A. I. Robles, K. Standahl Olsen, D. W. Tsui, V. Georgoulias, J. Creaney, K. Dobra, M. Vyberg, N. Minato, R. A. Anders, A. Børresen‑Dale, J. Zhou, Pål. Saetrom, B. Schnack Nielsen, M. B. Kirschner, H. E. Krokan, V. Papadimitrakopoulou, I. Tsamardinos, and O. D. Røe, “Excerpts from the 1st international NTNU symposium on current and future clinical biomarkers of cancer: innovation and implementation, June 16th and 17th 2016, Trondheim, Norway,” Journal of Translational Medicine, 2016.
    [Summary]

  • V. Lagani, S. Triantafillou, G. Ball, J. Tegner, and I. Tsamardinos, “Probabilistic computational causal discovery for systems biology,” Uncertainty in Biology, 2016.
    [Summary]

  • [DOI] V. Lagani, A. D. Karozou, D. Gomez-Cabrero, G. Silberberg, and I. Tsamardinos, “A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions,” BMC Bioinformatics, iss. S5, 2016.
    [Summary]

  • [DOI] N. Karathanasis, I. Tsamardinos, and V. Lagani, “omicsNPC: applying the NonParametric Combination methodology to the integrative analysis of heterogeneous omics data,” PloS one, 2016.
    [Summary]

  • [DOI] J. Goveia, A. Pircher, L. Conradi, J. Kalucka, V. Lagani, M. Dewerchin, G. Eelen, R. J. DeBerardinis, I. D. Wilson, and P. Carmeliet, “Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities,” EMBO Molecular Medicine, 2016.
    [Summary]

  • [DOI] P. Charonyktakis, M. Plakia, I. Tsamardinos, and M. Papadopouli, “On user-centric modular QoE prediction for voip based on machine-learning algorithms,” IEEE Transactions on Mobile Computing, 2016.
    [Summary]

  • G. Borboudakis and I. Tsamardinos, “Towards Robust and Versatile Causal Discovery forBusiness Applications.” 2016.
    [Summary]

  • G. Athineou, G. Papoutsoglou, S. Triantafullou, I. Basdekis, V. Lagani, and I. Tsamardinos, “SCENERY: a Web-Based Application for Network Reconstruction and Visualization of Cytometry Data.,” Accepted for publication on the 10th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2016)., 2016.
    [Summary]

2015

  • I. Tsamardinos, A. Rakhshani, and V. Lagani, “Performance-estimation properties of cross-validation based protocols with simultaneous hyper-parameter optimization,” International Journal on Artificial Intelligence Tools, 2015.
    [Summary]

  • I. Tsamardinos, M. Tsagris, and V. Lagani, “Feature selection for longitudinal data,” Proceedings of the 10th conference of the Hellenic Society for Computational Biology & Bioinformatics (HSCBB15), iss. 1, 2015.
    [Summary]

  • V. Lagani, F. Chiarugi, D. Manousos, V. Verma, J. Fursse, M. Kostas, and I. Tsamardinos, “Realization of a service for the long-term risk assessment of diabetes-related complications,” Journal of Diabetes and its Complications, iss. 5, 2015.
    [Summary]

  • V. Lagani, F. Chiarugi, S. Thomson, J. Fursse, E. Lakasing, R. W. Jones, and I. Tsamardinos, “Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data,” Journal of Diabetes and its Complications 2015., iss. 4, 2015.
    [Summary]

  • N. Karathanasis, I. Tsamardinos, and P. Poirazi, “MiRduplexSVM: A high-Performing miRNA-duplex prediction and evaluation methodology,” PloS one, iss. 5, 2015.
    [Summary]

  • G. Borboudakis and I. Tsamardinos, “Bayesian Network Learning with Discrete Case-Control Data.,” Uncertainty in Artificial Intelligence (UAI), 2015.
    [Summary]

  • A. Alexandridis, G. Borboudakis, and A. Mouchtaris, “Addressing the data-association problem for multiple sound source localization using DOA data estimates,” 23rd European Signal Processing Conference (EUSIPCO), 2015.
    [Summary]

2014

  • I. Tsamardinos, A. Rakhshani, and V. Lagani, “Performance-estimation properties of cross-validation-based protocols with simultaneous hyper-parameter optimization,” 8th Hellenic Conference on Artificial Intelligence (SETN 2014)., 2014.
    [Summary]

  • S. Triantafilou, I. Tsamardinos, and A. Roumpelaki, “Learning Neighborhoods of High Confidence in Constraint-Based Causal Discovery.,” Springer 2014, 2014.
    [Summary]

  • S. Triantafillou and I. Tsamardinos, “Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets,” Journal of Machine Learning Research, 2014.
    [Summary]

  • C. Papagiannopoulou, G. Tsoumakas, and I. Tsamardinos, “Discovering and Exploiting Entailment Relationships in Multi-Label Learning,” ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2015 (KDD), 2014.
    [Summary]

  • K. Kerkentzes, V. Lagani, I. Tsamardinos, M. Vyberg, and O. Røe, “Hidden treasures in “ancient” microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal,” Frontiers 2014, iss. 251, 2014.
    [Summary]

  • N. Karathanasis, I. Tsamardinos, and P. Poirazi, “Don’t use a cannon to kill the miRNA mosquito,” Bioinformatics, iss. 7, 2014.
    [Summary]

  • G. T. Huang, I. Tsamardinos, V. Raghu, N. Kaminski, and P. V. Benos, “T-ReCS: stable selection of dynamically formed groups of features with application to prediction of clinical outcomes.,” Pacific Symposium on Biocomputing (PSB), 2014.
    [Summary]

2013

  • G. L. Papadopoulos, E. Karkoulia, I. Tsamardinos, C. Porcher, J. Ragoussis, J. Bungert, and J. Strouboulis, “GATA-1 genome-wide occupancy associates with distinct epigenetic profiles in mouse fetal liver erythropoiesis,” Nucl. Acids Res, iss. 9, 2013.
    [Summary]

  • V. Lagani, L. Koumakis, F. Chiarugi, E. Lakasing, and I. Tsamardinos, “A systematic review of predictive risk models for diabetes complications based on large scale clinical studies,” Journal of Diabetes and Its Complications, iss. 4, 2013.
    [Summary]

  • V. Lagani, G. Kortas, and I. Tsamardinos, “Biomarker signature identification in “omics” data with multi-class outcome,” Computational and Structural Biotechnology Journal, 2013, 6(7), iss. 7, 2013.
    [Summary]

  • N. Karathanasis, I. Tsamardinos, and P. Poirazi, “A bioinformatics approach for investigating the determinants of Drosha processing,” 13th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2013), 2013.
    [Summary]

  • I. Karakasilioti, I. Kamileri, G. Chatzinikolaou, T. Kosteas, E. Vergadi, A. R. Robinson, I. Tsamardinos, T. A. Rozgaja, S. Siakouli, C. Tsatsanis, L. J. Niedernhofer, and G. A. Garinis, “DNA damage triggers a chronic autoinflammatory response, leading to fat depletion in NER progeria,” Cell Metabolism, iss. 3, 2013.
    [Summary]

  • P. Hunter, T. Chapman, P. Coveney, B. de Bono, V. Diaz, J. Fenner, A. Frangi, P. Harris, R. Hose, P. Kohl, P. Lawford, K. McCormack, M. Mendes, S. Omholt, A. Quarteroni, N. Shublaq, J. Skår, K. Stroetmann, J. Tegner, S. Thomas, I. Tollis, I. Tsamardinos, J. van Beek, and M. Viceconti, “A vision and strategy for the virtual physiological human: 2012 update,” Interface Focus, iss. 2, 2013.
    [Summary]

2012

  • I. Tsamardinos, S. Triantafillou, and V. Lagani, “Towards integrative causal analysis of heterogeneous data sets and studies,” Journal of Machine Learning Research, iss. 1, 2012.
    [Summary]

  • I. Tsamardinos, V. Lagani, and D. Pappas, “Discovering multiple, equivalent biomarker signatures.,” proceedings of the 7th conference of the Hellenic Society for Computational Biology & Bioinformatics, 2012.
    [Summary]

  • I. Tsamardinos, G. Borboudakis, E. Christodoulou, and O. D. Røe, “Chemosensitivity Prediction of Tumours Based on Expression, miRNA, and Proteomics Data,” International Journal of Systems Biology and Biomedical Technologies (IJSBBT), iss. 2, 2012.
    [Summary]

  • V. Lagani, I. Tsamardinos, and S. Triantafillou, “Learning from Mixture of Experimental Data: A Constraint–Based Approach,” Artificial Intelligence: Theories and Applications: 7th Hellenic Conference on AI, 2012.
    [Summary]

  • S. Kleisarchaki, D. Kotzinos, I. Tsamardinos, and V. Christophides, “A Methodological Framework for Statistical Analysis of Social Text Streams,” International Workshop on Information Search, Integration and Personalization (ISIP 2012), 2012.
    [Summary]

  • S. Dato, M. Soerensen, A. Montesanto, V. Lagani, G. Passarino, K. Christensen, and L. Christiansen, “UCP3 polymorphisms, hand grip performance and survival at old age: Association analysis in two Danish middle aged and elderly cohorts,” Joint Meeting AGI-SIBV-SIGA, iss. 8, 2012.
    [Summary]

  • S. Dato, A. Montesanto, V. Lagani, B. Jeune, K. Christensen, and G. Passarino, “Frailty phenotypes in the elderly based on cluster analysis: a longitudinal study of two Danish cohorts. Evidence for a genetic influence on frailty,” Age, iss. 3, 2012.
    [Summary]

  • T. I. Brown L.E. and D. Hardin, “To feature space and back: Identifying top-weighted features in polynomial support vector machine models,” Intelligent Data Analysis, iss. 4, 2012.
    [Summary]

  • G. Borboudakis, S. Triantafillou, and I. Tsamardinos, “Tools and algorithms for causally interpreting directed edges in maximal ancestral graphs,” Sixth European Workshop on Probabilistic Graphical Models, (PGM 2012), 2012.
    [Summary]

  • G. Borboudakis and I. Tsamardinos, “Scoring and searching over Bayesian networks with causal and associative priors,” Uncertainty in Artificial Intelligence (UAI), 2012.
    [Summary]

  • G. Borboudakis and I. Tsamardinos, “Incorporating causal prior knowledge as path-constraints in bayesian networks and maximal ancestral graphs,” Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012.
    [Summary]

  • A. P. Armen, I. Tsamardinos, N. Karathanasis, and P. Poirazi, “SVM-based miRNA: MiRNA∗ duplex prediction,” IEEE 12th International Conference on Bioinformatics and Bioengineering, BIBE 2012, 2012.
    [Summary]

2011

  • I. Tsamardinos, O. D. Roe, and V. Lagani, “Introducing Integrative Causal Analysis for Co-Analyzing Heterogeneous Studies with an Application to Methylation and Gene Expression Mesothelioma Cancer Data.,” 6th Conference of the Hellenic Society for Computational Biology and Bioinformatics (HSCBB11), 2011.
    [Summary]

  • V. Lagani, I. Tsamardinos, M. Grammatikou, and G. Garinis, “A Genome-Wide Study of the Effect of Aging on Level-2 Gene-Ontology Categories in Mice Using Mixed Models,” The 5th International Workshop on Data Mining in Functional Genomics and Proteomics: Current Trends and Future Directions, ECML PKDD 2011, 2011.
    [Summary]

  • V. Lagani, V. Kontogiannis, P. Argyropaidas, and C. Chronaki, “Use of SMS for Tsunami Early Warnings at a Table Top Exercise,” 4th ICST International Conference on eHealth (eHealth 2011), 2011.
    [Summary]

  • L. Koumakis, F. Chiarugi, V. Lagani, and I. Tsamardinos, “Risk assessment models for diabetes complications: A survey of available online tools,” 2nd International ICST Conference on Wireless Mobile Communication and Healthcare (MobiHealth 2011), 2011.
    [Summary]

  • C. Filippaki, G. Antoniou, and I. Tsamardinos, “Using constraint optimization for conflict resolution and detail control in activity recognition,” Second International Joint Conference on Ambient Intelligence 2011, 2011.
    [Summary]

  • E. G. Christodoulou, O. D. Røe, A. Folarin, and I. Tsamardinos, “Information-Preserving Techniques Improve Chemosensitivity Prediction of Tumours Based on Expression Profiles,” 12th Engineering Applications of Neural Networks (EANN) / 7th Artificial Intelligence Applications and Innovations (AIAI) joint conferences, Workshop on Computational Intelligence Applications in Bioinformatics (CIAB 2011), 2011.
    [Summary]

  • G. Borboudakis, S. Triantafilou, V. Lagani, and I. Tsamardinos, “A constraint-based approach to incorporate prior knowledge in causal models.,” Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2011.
    [Summary]

  • A. Armen and I. Tsamardinos, “A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification.,” Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2011.
    [Summary]

2010

  • I. Tsamardinos and G. Borboudakis, “Permutation testing improves Bayesian network learning,” Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases: Part III, 2010.
    [Summary]

  • S. Triantafullou, I. Tsamardinos, and I. G. Tollis, “Learning causal structure from overlapping variable sets,” Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
    [Summary]

  • V. Lagani and I. Tsamardinos, “Structure-based variable selection for survival data,” Bioinformatics, iss. 15, 2010.
    [Summary]

  • P. Hunter, P. V. Coveney, B. de Bono, V. Diaz, J. Fenner, A. F. Frangi, P. Harris, R. Hose, P. Kohl, P. Lawford, K. McCormack, M. Mendes, S. Omholt, A. Quarteroni, J. Skar, J. Tegner, S. Randall, I. G. Tollis, I. Tsamardinos, and M. van Beek, “A vision and strategy for the virtual physiological human in 2010 and beyond,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, iss. 1920, 2010.
    [Summary]

  • K. Gkirtzou, I. Tsamardinos, P. Tsakalides, and P. Poirazi, “MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors,” PloS one, iss. 8, 2010.
    [Summary]

  • F. Chiarugi, D. Emmanouilidou, and I. Tsamardinos, “The morphological classification of heartbeats as dominant and non-dominant in ECG signals,” Physiological Measurement, iss. 5, 2010.
    [Summary]

  • C. F. Aliferis, A. R. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, “Local causal and markov blanket induction for causal discovery and feature selection for classification part ii: Analysis and extensions,” The Journal of Machine Learning Research, 2010.
    [Summary]

  • C. F. Aliferis, A. R. Statnikov, I. Tsamardinos, S. Mani, and X. D. Koutsoukos, “Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Analysis and extensions,” The Journal of Machine Learning Research, 2010.
    [Summary]

2009

  • I. Tsamardinos and S. Triantafillou, “The possibility of integrative causal analysis: Learning from different datasets and studies,” Journal of Engineering Intelligent Systems, iss. 2-3, 2009.
    [Summary]

  • I. Tsamardinos and A. Mariglis, “Multi-source causal analysis: Learning Bayesian networks from multiple datasets,” Artificial Intelligence Applications and Innovations III, Proceedings of the 5TH IFIP Conference on Artificial Intelligence Applications and Innovations, 2009.
    [Summary]

  • C. F. Aliferis, A. R. Statnikov, I. Tsamardinos, J. S. Schildcrout, B. E. Shepherd, and J. F. E. Harrell, “Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data,” PloS one, iss. 3, 2009.
    [Summary]

2008

  • I. Tsamardinos and L. Brown, “Bounding the False Discovery Rate in Local Bayesian Network Learning.,” Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008.
    [Summary]

  • F. Chiarugi, D. Emmanouilidou, I. Tsamardinos, and I. G. Tollis, “Morphological classification of heartbeats using similarity features and a two-phase decision tree,” Computers in Cardiology, 2008.
    [Summary]

  • L. Brown and I. Tsamardinos, “A Strategy for Making Predictions Under Manipulation.,” In JMLR: Workshop and Conference Proceedings, 2008.
    [Summary]

2006

  • I. Tsamardinos, L. Brown, and C. Aliferis, “The max-min hill-climbing Bayesian network structure learning algorithm,” Machine learning, iss. 1, 2006.
    [Summary]

  • I. Tsamardinos, A. Statnikov, L. Brown, and C. Aliferis, “Generating Realistic Large Bayesian Networks by Tiling.,” In The 19th International FLAIRS Conference, 2006.
    [Summary]

  • C. Aliferis, A. Statnikov, and I. Tsamardinos, “Challenges in the analysis of mass-throughput data: a technical commentary from the statistical machine learning perspective,” Cancer Informatics, 2006.
    [Summary]

2005

  • A. Statnikov, I. Tsamardinos, Y. Dosbayev, and C. F. Aliferis, “GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data,” International journal of medical informatics, iss. 7-8, 2005.
    [Summary]

  • A. Statnikov, C. F. Aliferis, I. Tsamardinos, D. Hardin, and S. Levy, “A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis,” Bioinformatics, iss. 5, 2005.
    [Summary]

  • M. Pollack and I. Tsamardinos, “Efficiently dispatching plans encoded as simple temporal problems,” Intelligent Techniques for Planning, 2005.
    [Summary]

  • L. Brown, I. Tsamardinos, and C. Aliferis, “A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms,” In Proceedings of the national conference on artificial intelligence, iss. 2, 2005.
    [Summary]

  • Y. Aphinyanaphongs, I. Tsamardinos, A. Statnikov, D. Hardin, and C. F. Aliferis, “Text categorization models for high-quality article retrieval in internal medicine,” Journal of the American Medical Informatics Association, iss. 2, 2005.
    [Summary]

2004

  • A. Statnikov, C. Aliferis, and I. Tsamardinos, “Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development,” in Proceedings of the 11th World Congress in Medical Informatics (MEDINFO ’04), iss. 2, 2004.
    [Summary]

  • D. Hardin, I. Tsamardinos, and C. Aliferis, “A theoretical characterization of linear SVM-based feature selection,” The Twenty-First International Conference on Machine Learning (ICML 2004), 2004.
    [Summary]

  • L. Brown, I. Tsamardinos, and C. Aliferis, “A novel algorithm for scalable and accurate Bayesian network learning,” in Proceedings of 11th World Congress in Medical Informatics (MEDINFO ’04), iss. 1, 2004.
    [Summary]

2003

  • I. Tsamardinos and C. Aliferis, “Towards Principled Feature Selection: Relevancy, Filters and Wrappers..” 2003.
    [Summary]

  • I. Tsamardinos, M. E. Pollack, and S. Ramakrishnan, “Assessing the probability of legal execution of plans with temporal uncertainty,” ICAPS03 Workshop on Planning under Un-certainty and Incomplete Information, 2003.
    [Summary]

  • I. Tsamardinos and M. Pollack, “Efficient solution techniques for disjunctive temporal reasoning problems,” Artificial Intelligence, iss. 1-2, 2003.
    [Summary]

  • I. Tsamardinos, C. Aliferis, and A. Statnikov, “Time and sample efficient discovery of Markov blankets and direct causal relations,” The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), 2003.
    [Summary]

  • I. Tsamardinos, T. Vidal, and M. Pollack, “CTP: A new constraint-based formalism for conditional, temporal planning,” Special Issue on Planning of Constraints Journal, iss. 4, 2003.
    [Summary]

  • I. Tsamardinos, C. Aliferis, A. Statnikov, and E. Statnikov, “Algorithms for Large Scale Markov Blanket Discovery.,” FLAIRS conference, 2003.
    [Summary]

  • M. E. Pollack, L. Brown, D. Colbry, C. E. McCarthy, C. Orosz, B. Peintner, S. Ramakrishnan, and I. Tsamardinos, “Autominder: An intelligent cognitive orthotic system for people with memory impairment,” Robotics and Autonomous Systems, iss. 3-4, 2003.
    [Summary]

  • L. Frey, D. Fisher, I. Tsamardinos, C. F. Aliferis, and A. Statnikov, “Identifying Markov blankets with decision tree induction,” The Third IEEE International Conference on Data Mining (ICDM’03), 2003.
    [Summary]

  • C. F. Aliferis, I. Tsamardinos, and A. Statnikov, “HITON: a novel Markov Blanket algorithm for optimal variable selection,” the American Medical Informatics Association meeting 2003 (AMIA 2003), 2003.
    [Summary]

  • C. Aliferis, I. Tsamardinos, A. Statnikov, and L. Brown, “Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery.,” International Conference on Mathematics and Engineering Techniques in Medicine and Biolog-ical Sciences (METMBS ’03), 2003.
    [Summary]

  • C. F. Aliferis, I. Tsamardinos, P. Massion, A. Statnikov, and D. Hardin, “Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation of Explanatory Factors.,” International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS ’03), 2003.
    [Summary]

2002

  • M. E. Pollack, C. E. McCarthy, S. Ramakrishnan, and I. Tsamardinos, “Execution-time plan management for a cognitive orthotic system,” Plan-Based Control of Robotic Agents, 2002.
    [Summary]

  • A. Berfield, P. K. Chrysanthis, I. Tsamardinos, M. E. Pollack, and S. Banerjee, “A scheme for integrating e-services in establishing virtual enterprises,” 12th International Workshop on Research Issues on Data Engineering (RIDE-02), 2002.
    [Summary]

  • M. E. Pollack, C. E. McCarthy, S. Ramakrishnan, I. Tsamardinos, L. Brown, S. Carrion, D. Colbry, C. Orosz, and B. Peintner, “Autominder: A planning, monitoring, and reminding assistive agent,” Proceedings of the 7th International Conference on Intelligent Autonomous Systems (IAS), 2002.
    [Summary]

  • C. F. Aliferis, I. Tsamardinos, P. Mansion, A. Statnikov, and D. Hardin, “Machine learning models for lung cancer classification using array comparative genomic hybridization.,” 16th International FLAIRS Conference, 2002.
    [Summary]

  • I. Tsamardinos, “A probabilistic approach to robust execution of temporal plans with uncertainty,” Proceedings of the 2nd Greek National Conference on Artificial Intelligence, 2002.
    [Summary]

2001

  • I. Tsamardinos, M. Pollack, and P. Ganchev, “Flexible dispatch of disjunctive plans,” Proceedings of Sixth European Conference on Planning 2001 (ECP-01), 2001.
    [Summary]

2000

  • I. Tsamardinos, M. Pollack, and J. Horty, “Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches.,” Proceedings of the 5th International Conference on AI Planning and Scheduling (AIPS 2000), Breckenridge, CO, April, 2000, 2000.
    [Summary]

1999

  • M. Pollack, I. Tsamardinos, and J. Horty, “Adjustable autonomy for a plan management agent,” Proceedings of the 1999 AAAI Spring Symposium on Adjustable Autonomy, 1999.
    [Summary]

1998

  • C. Bicchieri, M. Pollack, C. Rovelli, and I. Tsamardinos, “Bicchieri-The\_Potential\_for\_the\_Evolution\_of\_Co-operatin.pdf,” International Journal of Computer-Human Systems, iss. 1, 1998.
    [Summary]

  • I. Tsamardinos, N. Muscettola, and P. Morris, “Fast transformation of temporal plans for efficient execution,” Proceedings of the 15th National Conference on Artificial Intelli-gence (AAAI’98), 1998.
    [Summary]

  • N. Muscettola, P. Morris, and I. Tsamardinos, “Reformulating temporal plans for efficient execution,” Proceedings of the 6th Conference Principles of Knowledge Represen-tation and Reasoning (KR), 1998.
    [Summary]

1995

  • S. Orphanoudakis, M. Tsiknakis, C. Chronaki, S. Kostomanolakis, M. Zikos, and I. Tsamardinos, “Development of an Integrated Image Management and Communication System on Crete,” Proceedings of Computed Aided Radiology ’95, 1995.
    [Summary]