Biomedical and Health Informatics Team (BioHIT)

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About BioHIT

Biomedical and Health Informatics Team (BioHIT) is a meeting team where members and invited speakers give presentations and participate in conversations about biomedical and health informatics, bioinformatics and related fields.

BioHIT meetings begun on April 2016 (initially as BioCAKT meetings) and since then a new presentation take place in Institute of Informatics and Telecommunications, NCSR D, twice a month.

Previous presentations details and relative material are available in this page.

BioHIT seminar presentations

  • Μελέτη αναπαραστάσεων γονιδιωματικών ακολουθιών σε προβλήματα ταξινόμησης.

    Η παρουσίαση θα αναφερθεί στη βιβλιογραφική έρευνα που πραγματοποιήθηκε στα πλαίσια μεταπτυχιακής διπλωματικής εργασίας με θέμα διαφορετικούς τρόπους αναπαράστασης DNA ακολουθιών. Σκοπός της διπλωματικής είναι η αξιολόγηση της απόδοσης των αναπαραστάσεων σε προβλήματα ταξινόμησης.

    Μαρίνα-Αγάπη Αθανασούλη, 20/12/2017 (Slides)

    Η Μαρίνα-Αγάπη Αθανασούλη είναι μεταπτυχιακή φοιτήτρια Βιοπληροφορικής του τμήματος Βιολογίας του ΕΚΠΑ, όπου εκπονεί τη διπλωματική της εργασία με θέμα τη Μελέτη αναπαραστάσεων γονιδιωματικών ακολουθιών σε προβλήματα ταξινόμησης. Είναι πτυχιούχος του τμήματος Επιστήμης Φυτικής Παραγωγής του Γεωπονικού Πανεπιστημίου Αθηνών, όπου η πτυχιακή της εργασία αφορούσε την αξιολόγηση γενετικής παραλλακτικότητας τοπικών ποικιλιών Vicia ervilia L. με τη χρήση μοριακών δεικτών. Συνεργάζεται με το εργαστήριο Τεχνολογίας Γνώσεων και Λογισμικού του ΙΠΤ του ΕΚΕΦΕ Δημόκριτος.

  • The In Silico Oncology and In Silico Medicine Group of ICCS-SECE-NTUA and the Large Scale EU-US Research Project CHIC on In Silico Oncology

    In silico medicine, an emergent scientific and technological domain based on clinically driven and clinically oriented multiscale biomodeling, appears to be the latest trend regarding the translation of mathematical and computational biological science to clinical practice through massive exploitation of information technology. In silico (i.e. on the computer) experimentation for each individual patient using their own multiscale biomedical data is expected to significantly improve the effectiveness of treatment in the future, since reliable computer predictions could suggest the optimal treatment scheme(s) and schedules(s) for each separate case. In this context, a brief outline of the structure and the activities of the In Silico Oncology and In Silico Medicine Group (ISO&ISM_G), Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering (SECE), National Technical University of Athens (NTUA) will be presented. Additionally, the large scale EU-US integrating research project CHIC entitled: “CHIC - Computational Horizons in Cancer: Developing Meta- and Hyper-Multiscale Models and Repositories for In Silico Oncology” that was funded by the European Commission (EC) and coordinated by ISO&ISM_G-ICCS-NTUA will be outlined. Within the framework of CHIC, a number of highly innovative meta- and hyper-multiscale models and repositories for in silico oncology were developed, clinically adapted and partly clinically validated. Complex technological systems supporting and facilitating the process of hypermodel building and execution aiming at treatment personalization were also developed and successfully tested. It is noted that the outcome of the CHIC project was both assessed as “excellent” and denoted as “great achievements” by EC.

    Dr. Georgios S. Stamatakos, 18/12/2017

    Georgios S. Stamatakos (www.ece.ntua.gr/en/staff/400) is Research Professor of Analysis and Simulation of Biological Systems and their Interaction with Electromagnetic Radiation at the Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering (SECE), National Technical University of Athens (NTUA). He is also a Visiting Professor at the School of Electrical and Computer Engineering, NTUA. He has founded and directs the In Silico Oncology and In Silico Medicine Group of ICCS-SECE-NTUA (www.in-silico-oncology.iccs.ntua.gr). He holds a Diploma/MSc degree in electrical engineering from NTUA, an MSc degree in bioengineering from the University of Strathclyde, Glasgow, UK, and a Ph.D. degree in physics (biophysics) from NTUA. He proposed the concept and the system of "Oncosimulator” as well as the term and the concept of “in silico oncology”. The latter denotes a new clinical trial driven scientific and technological discipline, primarily based on multiscale mechanistic biomodelling and in silico experimentation (i.e. experimentation on the computer). In silico oncology has also proved a precursor of the generic in silico medicine (https://en.wikipedia.org/wiki/In_silico_medicine). He pioneered the development of the Oncosimulator of the European Commission (EC) and Japan co-funded ACGT Integrated Project, a "world first" (http://cordis.europa.eu/result/rcn/86061_en.html). G. Stamatakos was the scientific and the overall coordinator as well as the leader of the fundamental science part of the large scale EU-US integrating research project CHIC on in silico oncology (http://www.chic-vph.eu/). The outcome of the latter was assessed by EC as excellent. His research interests include in silico oncology, in silico medicine, multiscale cancer modeling, computational medicine, systems medicine, precision medicine, the Virtual Physiological Human (VPH) initiative, systems biology, bioinformatics, biomedical engineering, bioengineering, bioelectromagnetics, biooptics, computational electromagnetics, software engineering and applied mathematics.

  • Enabling reproducibility in critical care research

    There is an increasing demand for authors of research publications to provide sufficient detail to enable readers to reproduce the reported results. When studies are reproducible they become building blocks for future research, for example by acting as tutorials for carrying out analyses and by providing reusable analytical code. In this presentation, we highlight ongoing efforts at the MIT Laboratory for Computational Physiology to work towards reproducibility in critical care research. We present several freely available critical care datasets shared by the laboratory, including the Medical Information Mart for Intensive Care (MIMIC), and we discuss our experiences of hosting international 'datathons'. We also report on a recent study in which we attempt to reproduce the cohorts of 28 published mortality prediction studies that use MIMIC. We discuss the challenges in reproducing the cohorts, highlighting the importance of clearly reported methods (e.g. data cleansing, variable selection, cohort selection) and the need for open code and publicly available benchmarks.

    Dr. Tom Pollard, 07/09/2017 (Slides)

    Dr. Tom Pollard is a Research Scientist at the Massachusetts Institute of Technology (MIT) Laboratory for Computational Physiology. Most recently he has been working with colleagues to release the [eICU Collaborative Research Database] (http://eicu-crd.mit.edu/), a freely-accessible database comprising patient data collected from critical care units across the US. Prior to joining MIT in 2015, Tom completed his PhD at University College London, UK, where he explored models of health in critical care patients. He has a broad interest in how we can improve the way that health data is collected and reused for the benefit of patients. He is a Fellow of the Software Sustainability Institute and a member of the MIT Task Force on Open Access."

  • Atom Mapping of Chemical Reactions

    A chemical reaction transforms a set of molecules -reactants- into a different set of molecules -products- by rearranging the atoms. The number and the species of atoms remain unchanged during the reaction. Therefore there is a one-to-one correspondence between the atoms in reactants and the atoms in products. The atom mapping problem consists of finding this correspondence. In this presentation, an optimization based approach to the atom mapping problem will be presented. Also, directions for improving that approach will be discussed such as the use of Machine Learning for incorporating chemical knowledge in the optimization problem.

    Eleni Litsa, 17/07/2017

    Eleni Litsa is a first year PhD student in a joint PhD program between the Rice University and Demokritos. She is co-advised by Professor Lydia Kavraki in Rice and Dr. George Giannakopoulos in Demokritos. She has graduated from the Electrical and Computer Engineering department in the National Technical University of Athens. Her research interests include Bioinformatics/Chemoinformatics and Machine Learning.

  • Γενετική του καρκίνου μαστού-ωοθηκών

    Η παρουσίαση θα ασχοληθεί με τους γενετικούς παράγοντες που σχετίζονται με την εμφάνιση των καρκίνων του μαστού και των ωοθηκών.
    Οι παράγοντες αυτοί είναι μεταλλάξεις σε γονίδια που έχουν σχέση με την επιδιόρθωση του γενετικού υλικού, όπως τα γονίδια BRCA1, BRCA2, TP53, PALB2 κ.α.
    Οι μεταλλάξεις αυτές κληρονομούνται και κληροδοτούνται, με τον αυτοσωμικό επικρατή τρόπο, από γενιά σε γενιά και έχουν ως αποτέλεσμα την εμφάνιση διαφόρων καρκίνων (κυρίως μαστού-ωοθηκών) σε διαφορετικές γενιές μιας οικογένειας. Η εμφάνιση των καρκίνων αυτών συμβαίνει συνήθως σε νέες ηλικίες, κάτω των 45 χρόνων. Η διεισδυτικότητα των μεταλλάξεων αυτών κυμαίνεται από 50-85%, δηλαδή 50-85% των γυναικών που φέρουν μία μετάλλαξη θα αναπτύξουν καρκίνο μαστού η ωοθηκών μέχρι την ηλικία των 80 χρόνων.
    Όσον αφορά τη μεθοδολογία, η ανάλυση του γενετικού υλικού γίνεται με χρήση της τεχνολογίας αλληλούχισης επόμενης γενεάς, ενώ χρησιμοποιούνται διάφορα εργαλεία Βιοπληροφορικής για την επεξεργασία των δεδομένων που προκύπτουν.

    Dr. Drakoulis Yannoukakos, 8/2/2017 (Slides)

    Dr Drakoulis Yannoukakos, is the Director of Molecular Diagnostics laboratory at the NCSR Demokritos. He studied at Aristotle University of Thessaloniki where he obtained his Bachelor in Chemistry and then obtained a Ph.D. in Biochemistry from the Université de Paris XII in France. He trained as a Post-doctoral fellow in the Department of Molecular Medicine, Beth Israel Hospital, Harvard University Medical School (1991-1994) and later on in the Department of Molecular Pharmacology, New York University Medical School (1994-1995).
    His research in Cancer Genetics focuses on the analysis of hereditary cancer syndromes such as Hereditary Breast-Ovarian Cancer, Lynch syndrome, Li-Fraumeni, Peutz-Jeghers and others. He is using targeted and exome sequencing in order to identify new cancer predisposing alleles.
    He has co-authored 103 peer-reviewed scientific publications with most of them being related to Cancer Genetics. He has served as a reviewer of international research journals (e.g. Nature Genetics) and funding agencies. He has received grants from the Greek General Secretariat of Research and Technology, the Ministry of Health, the Hellenic Cooperative Oncology Group, the Hellenic Society of Medical Oncologists, the Association of Women with Breast Cancer “Alma Zois” and the European Union. He participates at major international consortia such as Consortium of Investigators of Modifiers of BRCA1 and BRCA2, Breast Cancer Association Consortium, Evidence-based Network for the Interpretation of Germline Mutant Alleles and Ovarian Cancer Association Consortium.
    Dr Yannoukakos has taught graduate courses of Human Molecular Genetics at the Department of Medicine University of Thessaly and the Department of Biology, University of Athens. He is presently directing a research group of approximately 12 researchers.
    Full CV: here

  • RADIO: Unobtrusive, Efficient, Reliable and Modular Solutions for Independent Ageing

    Technical advancements in ICT, including robotics, bring new opportunities for the ageing population of Europe, the healthcare systems, as well as the European companies providing relevant technology and services at the global scale.
    RADIO pursues a novel approach to acceptance and unobtrusiveness: a system where sensing equipment is not discrete but an obvious and accepted part of the user's daily life. By using the integrated smart home/assistant robot system as the sensing equipment for health monitoring, we divert the users' attention from the functionality of the sensors rather than from the sensors themselves. In this manner, sensors do not need to be discrete and distant or masked and cumbersome to install; they do however need to be perceived as a natural component of the smart home/assistant robot functionalities.

    Dr. Stasinos Konstantopoulos, 26/10/2016 (Slides, RADIO)

    Dr Stasinos Konstantopoulos, MEng in Computer Engineering and Informatics (University of Patras, Greece, 1997), MSc in Artificial Intelligence (Edinburgh University, U.K., 1998), PhD on Computational Logic and Language Technology (Groningen University, the Netherlands, 2003) has been affiliated to the Software and Knowledge Engineering Lab, Institute of Informatics & Telecommunications, NCSR "Demokritos" since 2004.
    His research interests are artificial intelligence and data management where he has published extensively, has reviewed for journals, and has been on the programme and organizing committees of conferences including chairing the programme committee of the 2010 Conference of the Greek AI Society (SETN 2010). Since 2012 Stasinos has been leading core technical work packages in Intelligent Data Mangement projects. He also currently leads Roboskel, the robotics group of the Software and Knowledge Engineering Lab, which is mainly involved in RADIO on behalf on NCSR.

  • The Thalamic Visual Prosthesis Project

    The field of visual prosthetics has concentrated primarily on two targets for stimulation, the retina and the primary visual cortex. The lateral geniculate nucleus of the thalamus, the relay station between these two areas, has been largely ignored because of the difficulty of surgical approach. The development of deep brain stimulation techniques for addressing pathologies of the midbrain has opened surgical access to the thalamus, and motivates a reconsideration of targets for visual prosthetics.
    With this background, we have performed experiments in an animal model to demonstrate a proof of concept for a visual prosthesis based on thalamic microstimulation, followed by an experiment in a computer model to set basic engineering parameters for a thalamic visual prosthesis, in turn followed by a series of experiments with sighted humans to assess design performance. In this presentation we will review the compelling motivation for the thalamic approach, review the experimental results thus far, and provide a preview of future work.

    Dr. John S. Pezaris, 20/10/2016 (Slides)

    Dr. Pezaris has a background in computer science and electrical engineering with degrees from MIT, and in neurophysiology with a doctorate from California Institute of Technology, and post-doctoral experience at Harvard Medical School. He currently leads a research laboratory in the Neurosurgery Department at Massachusetts General Hospital with an appointment at Harvard Medical School. He will be visiting the University of Athens for Spring 2017 as a Fulbright Scholar.

  • Όταν το p-value εξορίστηκε ή τι να ΜΗΝ κάνω όταν κάνω έρευνα

    Θα μιλήσουμε συνοπτικά για συνήθη λάθη στην ερευνητική μεθοδολογία και τη στήριξη υποθέσεων,που έχουν οδηγήσει σε πληθώρα εργασιών που αλληλοσυγκρούονται. Επίσης, θα προτείνουμε τρόπους για πιο σωστή αξιολόγηση και ερμηνεία αποτελεσμάτων και σχετικές κατευθύνσεις, κυρίως σε τομείς με έντονη υποκειμενικότητα (π.χ. αξιολόγηση περιλήψεων)

    Δρ. Γιώργος Γιαννακόπουλος, 21/9/2016 (Slides)

  • CRISPR/Cas based technology : Taking Gene Editing to the next level

    Observation of bacteria and archaea's adaptive immune systems led to the development of the CRISPR/Cas* technique for genomic manipulation. The technique offers several advantages over previously used strategies and has been rapidly gaining in popularity, creating a need for new software tools. This is an introduction in basic CRISPR biology as well as CRISPR technology and its applications, highlighting genome editing approaches aiming to correct mutations that cause Duchenne Muscular Dystrophy (DMD).

    *CRISPR/Cas = Clustered Regularly Interspaced Short Palindromic Repeats/Crispr-associated nuclease

    Δήμητρα Σαξώνη, 31/8/2016 (Slides)

  • Drug Repositioning: Existing approaches and applicability for Duchenne Muscular Dystrophy

    Duchenne Muscular Dystrophy (DMD) is a severe progressive muscle-wasting disease caused by the absence of a functional dystrophin protein. Currently, no cure is available for DMD and among treatment options, only the use of corticosteroids has shown a clear beneficial effect. As novel drug development is a long procedure, identifying repositioning opportunities for existing drugs in DMD treatment, emerges as an appealing alternative. This is an introduction in existing approaches used for Drug Repositioning, with an emphasis on computational ones, under the perspective of applicability for DMD.

    Αναστάσιος Νεντίδης, 18/7/2016 (Slides)

  • Approaches to the atom mapping problem: A review

    A chemical reaction is a transformation converting reactants into products. The transformation consists of bond breakages and bond formations in reactants. The total number and the type of atoms are preserved during the reaction. Thus, there is a one to one correspondence between the atoms in reactants and the atoms in products. The atom mapping problem consists of finding this correspondence. If each chemical compound is represented by a graph structure then the atom mapping problem can be seen as a graph matching problem. This presentation is a review on the approaches to the atom mapping problem.

    Ελένη Λίτσα, 29/6/2016 (Slides)

  • Ευρετηρίαση ακολουθιών DNA με τη χρήση χαρακτηριστικών από γράφους Ν-γραμμάτων

    Η ευρετηρίαση ακολουθιών DNA αποτελεί μια σημαντική υπολογιστική πρόκληση λόγω του μεγάλου όγκου των δεδομένων που εμπλέκονται και της απαίτησης για αποδοτικές υλοποιήσεις. Στην παρουσίαση αυτή θα δούμε συνοπτικά τι είναι οι γράφοι Ν-γραμμάτων και πώς μπορούμε να τους χρησιμοποιήσουμε για να εξάγουμε αποδοτικές αναπαραστάσεις για αυτό το σκοπό.

    Βασίλης Χαρισόπουλος, 8/6/2016 (Slides)

  • Using Text Mining to Discover Repositioning Opportunities for Orphan Drugs and Rare Diseases

    On September 21, 2015, The FDA announced that it awarded 18 research grants totaling more than $19 million from the Orphan Products Grants program, to study and treat rare diseases that affect over 30 million people. While 30 million seems like a large number of patients, this is spread over 7000 rare diseases, where a disease that impacts fewer than 200,000 people in the United States is classfied as a rare disorder. Because these diseases affect so few, the little information that's available, is buried in a vast bulk of literature on well-studied conditions affecting larger numbers of patients. This aspect makes the problem of identification of relationships between rare diseases and drugs a good candidate for text mining. In this webinar, we will introduce text mining as a methodology and discuss how it can provide an advantage over traditional keywords searching for rare diseases and orphan drugs. We will also review how to use text mining can be used to identify rare diseases and their relationships to potential target genes and potential treatment in the published literature, as well as relationships of orphan drugs to new disease indications.

    Dr. Sherri Matis-Mitchell, 25/5/2016 (Slides)

  • Machine learning classification of autopsy proven vascular cognitive impairment and Alzheimer's disease using connected speech sampling

    This talk aims to present recent research advances in the early detection and classification of various types of dementia patients employing computational linguistics methods. Moreover, although Vascular Dementia (VaD) and Alzheimer's Disease (AD) are associated with changes in spoken language, these have not been extensively examined or compared. In the presented study: (1) the presence of differences in language produced by AD and VaD is confirmed using quantitative methods of evaluation, and (2) the most informative sources of variation between the groups is ascertained. A computational approach was adopted for the comparison of syntactic complexity and lexical variation in digitized transcripts of speech produced by AD and VaD patients and by age-matched cognitively normal controls (NC). We used machine learning text classification approaches to assign the samples to one of the three groups - AD, VaD and NC. The classification results illustrate the specific language idiosyncrasies and the word content specific differences of the three groups. Experimental results indicate that VaDs are found to exhibit a marked reduction in lexical variation and complexity compared to their AD counterparts.

    Dr. Vassiliki Rentoumi, 11/5/2016 (Slides)

    Dr. Vassiliki Rentoumi has worked as Post – Doctoral Researcher at Saint George’s University of London in the Clinical Sciences department and at the Software and Knowledge Engineering Laboratory (SKEL) at NCSR Demokritos in Athens.

  • BioASQ: "A challenge on large-scale biomedical semantic indexing and question answering"

    Η παρουσίαση είναι μια εισαγωγή στο διαγωνισμό BioASQ, στη δομή του σε επιμέρους δραστηριότητες και στην υποδομή για τη διεξαγωγή του.

    Αναστάσιος Νεντίδης, 27/4/2016 (Slides, BioASQ)

  • Reaction Map: An efficient atom mapping algorithm for chemical reactions

    Η παρουσίαση αφορά μία αλγοριθμική προσέγγιση για το πρόβλημα της αντιστοίχισης των ατόμων μεταξύ αντιδρώντων και προϊόντων στις χημικές αντιδράσεις, με χρήση τεχνικών γράφων και μεθόδων βελτιστοποίησης.

    Ελένη Λίτσα, 13/4/2016 (Slides)

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