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Open positions available – CALLS FOR EXPRESSION OF INTEREST
Several contractual positions are open for our ERC Consolidator Grant that kicked off January 1st, 2015 and will run for the next 3 years. The grant is entitled “CAUSALPATH: Next Generation Causal Analysis: Inspired by the Induction of Biological Pathways from Cytometry Data“. ERC grants are prestigious European research programs that support scientific excellence and provide research autonomy and flexibility. The grant’s goals are: (a) basic research on causal discovery, causal analysis, and causal modeling, (b) application on de novo inducing signal pathways from mass cytometry data, and (c) design of automated tools for causal discovery and signal pathways. CAUSALPATH is coordinated by our group in the Computer Science Department of University of Crete in collaboration with the Computational Medicine Unit in Karolinska Insitutet. Our group is highly multi-disciplinary and multi-national focusing on data analysis and machine learning with an emphasis on biomedical data.
Further information can be found in the attached calls for expression of interest.
- Post-Doc position –
- PhD Candidate position –
- Web Application Development position –
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Συμμετοχή του κ. Τσαμαρδίνου στο συνέδριο Κλινικής και Μεταφραστικής…
Στο πλαίσιο του συνεδρίου Κλινικής και Μεταφραστικής Ογκολογίας που πραγματοποιείται στο Ξενοδοχείο “Aquila Atlantis” στο Ηράκλειο το διάστημα 17-20 Νοεμβρίου 2016, ο κ. Ιωάννης Τσαμαρδίνος θα πραγματοποιήσει στις 19/11 μεταξύ 11:30-13:00 ομιλία με θέμα “Αυτόματα εύρεση βιοδεικτών και βιοϋπογραφών σε βιολογικά δεδομένα”. Η διάλεξη θα ενταχθεί στο στρογγυλό τραπέζι: ” Βιοπληροφορική και Ογκολογία”.
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Talk announcement of Prof Jan Lemeire February 16th room…
title: “Causal structure learning under unfaithfulness.”
abstract:” Inferring from experimental data the causal relations among variables is a scientific important but challenging task. One of the approaches relies on the probabilistic independencies between variables. Algorithms have been designed that allow the construction of the causal model of the system. Although powerful, one of the fundamental assumptions – namely faithfulness – will be rarely met in practica. Faithfulness states that all independencies found in the data are a result of the causal structure and not from specific parameter configurations. The importance of this property for causal learning is clear: if indeed all independencies come from the causal structure, the independencies provide direct evidence on the causal relations. Unfaithfulness will however appear, as a result of specific parameterizations (e.g. deterministic relations) or as a result of the limited sample size which makes that some dependencies are not measurable (called near-to-unfaithfulness). We will discuss the different cases of unfaithfulness that disrupt the causal learning, what we can do about it and how to deal with limited data. Furthermore it seems to investigate the types of unfaithfulness cases that appear in the MassCausal data. Understanding it’s nature might help the causal learning process.”
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Invited speech by Yannis Pantazis
Between January 19 – 22, 2016, Yannis Pantazis attended as an invited speaker the “Uncertainty Quantification for Multiscale Stochastic Systems and Applications” workshop held at IPAM, UCLA. The title of the talk was “Sensitivity analysis, uncertainty quantification and inference in stochastic dynamics“.
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Συμμετοχή της MXM στο 13th Hellenic Congress of Clinical…
The 13th Hellenic Congress of Clinical Chemistry is held in Heraklion Crete, October 29-31, 2015.
MXM participates with an invited lecture by Professor Tsamardinos:
“Bionformatics advances for automating predictive biomarker and biosignatures from data”.
Program of the congress:
program.pdf .
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Ολοκλήρωση του STATegra project
The EU funded STATegra project has come to its end, after three years of intense research and development of novel statistical methods for the integration of heterogeneous omics data.
Information regarding the objectives of the project, the activities carried in these three years and the scientific and commercial outcomes are available on the project website: http://www.stategra.eu/
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Θερινό σχολείο STATegra 2015
The STATegra Summer School in Omics and Data Integration has been held in Benicassim, Spain, from the 7th to the 11th of September 2015, providing training to more than 30 among experimentalists and bioinformaticians in the analysis of multi-omics experiments.
Further information on the summer school website: http://stategrasummerschool.com/
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Συμμετοχή της MXM στο SMODIA 2015
The second edition of the “Statistical Methods for Omics Data Integration and Analysis” conference has been held in Valencia, September 14-16, 2015.
MXM has participated with a talk regarding application of data-integration methods for the analysis of public microarray data in Leukemia patients.
The program of the conference and further information on SMODIA2015
website: http://smodia2015.cipf.es/
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Ομιλία από την Karen Sachs, 3 September
“Use of single cell proteomics measurements for elucidation of biomolecular regulatory networks”
Speaker: Karen Sachs, Stanford University School of Medicine
Date: 03 September 2015 Time: 11:00 – 13:00
Location: Seminar Room I, FORTH, Heraklion, Crete
Host: Ioannis Tsamardinos
Abstract:
We have previously introduced the application of probabilistic models for elucidation of statistical relationships from single cell proteomic data (Sachs et al, Science 2005), an approach enabled by the key insight that each cell may be considered an observation of the underlying biological system. The application of this approach has been partially limited by the low dimensionality of the data modality (flow cytometry), which enabled measurements of only up to ~10 proteins of interest per cell. In 2011, we introduced a next generation single cell proteomic technology, mass cytometry or CyTOF (cytometry time of flight, Bendall et all, Science 2011), which enables quantification of 30-40 parameters per cell.
In this talk, I will describe the CyTOF technology and illuminate its advantages and potential pitfalls. I will then discuss my perspective on causal modeling of single cell data and what hurdles remain in this application.
Bio:
Her work involves probabilistic modeling of signal transduction pathways. She uses probabilistic approaches to study how different proteins in a signaling pathway depend upon and influence each other.
This information is extracted from data, in which these proteins have been measured many times, each time in a single cell, using a technology called “flow cytometry”.