2019

  • Y. Pantazis and I. Tsamardinos, “A Unified Approach for Sparse Dynamical System Inference from Temporal Measurements, (to appear),” Bioinformatics, 2019.
    [Summary]

2018

  • [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

  • K. Tsirlis, V. Lagani, S. Triantafillou, and I. Tsamardinos, “On Scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing Algorithm.” 2017.
    [Summary]

  • M. Tsagris, G. Borboudakis, V. Lagani, and I. Tsamardinos, “Constraint-based Causal Discovery with Mixed Data.” 2017.
    [Summary]

  • [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]

  • [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]

  • [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, 2017.
    [Summary]

  • 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, iss. 1, 2017.
    [Summary]

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

2016

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

  • 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]