Department of Health Informatics

The presentation used during the meeting (that was on 02.03.2022) can be downloaded from here.

The document that contains useful links and information can be downloaded by clicking here.

Dr. Melike Dönertaş will give a seminar on “Temporal changes in gene expression across individuals, organs, and cells” on 17 January at 13:40. The abstract of the talk and a short bio is shared below.

Bio:
Dr. Dönertaş is a postdoctoral researcher at the Leibniz Institute on Aging. She completed her BSc in Molecular Biology and Genetics and her MSc in Biological Sciences, both at METU. She completed her PhD at the University of Cambridge as an EMBL fellow. She worked as a postdoctoral researcher at the European Bioinformatics Institute (EMBL-EBI) and Max Planck Institute for Biology of Aging. She has worked on a variety of topics ranging from cheminformatics to evolutionary genomics and ancient DNA studies, but her main research focus is on ageing, age-related diseases, and anti-ageing interventions.

Abstract:
Unlike development, ageing is not thought of as a programmed process but a result of cellular and evolutionary stochastic events. Thus, comparative analysis of development and ageing periods can help understand the underlying mechanisms and characteristics of ageing. This seminar will summarise our recent work, comparing gene expression changes during postnatal development and ageing across individuals, organs, and cells. Using transcriptome datasets covering the whole lifespan, we study how the level and between-individual variability of gene expression changes with age. We first show that, in the human brain, increased heterogeneity is characteristic for ageing but not for development. Moreover, the temporal trend in gene expression during development does not necessarily continue to the ageing period, at which half of the expression trajectories are reversed. Studying this phenomenon in multiple tissues of mice, we found that these reversals are associated with tissue-specific functions and contribute to an interesting phenomenon that tissues diverge from each other during postnatal development but, during ageing, tend to converge towards similar expression levels. Lastly, using an external single-cell gene expression dataset, we study how tissue composition and cell-autonomous changes may contribute to this divergence-convergence pattern. Overall, our results highlight the loss of tissue- and potentially, cellular identity as a common aspect of ageing.

You can join the seminar on the Zoom platform through this link.

Can Fırtına will give a seminar on “Enabling Accurate, Fast, and Memory-Efficient Genome Analysis via Efficient and Intelligent Algorithms” on 10 January at 13:40. The abstract of the talk and a short bio is shared below.

Bio:
Can Fırtına is a Ph.D. student at ETH Zurich working with Prof. Onur Mutlu. He received B.Sc. and M.Sc. degrees in Computer Engineering from Bilkent University, in 2015 and 2018, respectively. His current research interests broadly span computational biology and computer architecture topics, including correcting sequencing errors, accurately and quickly identifying sequence similarities, and hardware/software co-design for accelerating bioinformatics applications and genomic data analysis.

Abstract:
Genome sequence analysis plays a pivotal role in enabling many medical and scientific advancements in personalized medicine, outbreak tracing, the understanding of evolution, and forensics. Modern genome sequencing technologies can rapidly generate massive amounts of genomics data at low cost. However, due to the current limitations of sequencing technologies, the analysis of genome sequencing data is currently bottlenecked by the computational and spatial overheads of existing systems and algorithms, as many of the steps in genome sequence analysis must process a large amount of data using computation. Moreover, as sequencing technologies advance, the growth in the rate that sequencing devices generate genomics data is far outpacing the corresponding growth in computational power, placing greater pressure on these bottlenecks. In this seminar, we provide an overview of our works in three main directions.

Assoc. Prof. Nurcan Tuncbag will give a seminar on “Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact” on 3 January at 13:40. The abstract of the talk and a short bio is shared below.

Bio:
Dr. Tuncbag is an associate professor at Koç University jointly in the Department of Chemical and Biological Engineering and School of Medicine. She received her undergraduate degree in Chemical Engineering from Istanbul Technical University, and her master and doctorate degrees from Koç University Computational Sciences and Engineering department. She did her post-doctoral research in Massachusetts Institute of Technology (MIT) Biological Engineering Department between 2010-2014. She carried out her academic studies at Middle East Technical University between 2014-2021. Her work in computational systems biology and bioinformatics has been awarded nationally and internationally by several academies, foundations and councils. She has been a member of the Global Young Academy since 2020. She develops computational approaches to serve in network medicine, network modeling, trans-omic data integration, single cell and bulk omic data analysis, and discovery of latent cancer driver mutations.

Abstract:
Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable translational potential is minor have escaped identification. Yet, when paired with other mutations in cis, such ‘latent driver’ mutations can drive cancer. Additionally, the additivity of co-occurring driver mutations in different genes (in trans) can lead to powerful oncogenic signal, encoding aggressive proliferation. We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of ~80,000 tumor sequences from the TCGA and AACR GENIE databases. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double mutations can increase oncogenic activity, hence obtain a better drug response (e.g., in PIK3CA), or they can promote resistance to the drugs (e.g., in EGFR). On the other hand, co-occurring double mutations on different genes can additively promote tumorigenesis through single or multiple pathways. They are mostly in primary tumors. Rare occurrences can be a signature of metastatic tumors. Interrogation of big genomic data and integration with large-scale small-molecule sensitivity data can provide deep patterns that are rare – but can prompt dramatic phenotypic alterations and serve as clinical signatures.

Dr. Gökçe Ertürkmen will give a seminar on “Integrated Care Approach for the Management of Chronic Diseases in Europe and Turkey” on 27 December at 13:40. The abstract of the talk and a short bio is shared below.

Bio:
Dr. Ertürkmen has obtained her BSc, MSc, PhD from the Computer Engineering Department of the Middle East Technical University. She has finalized her PhD study on Intelligent Healthcare Monitoring Systems based on Semantically Enriched Clinical Guidelines in June 2008. She has worked as the principal researcher in many EU funded R&D projects. She has coordinated ICT-287800 SALUS project, which addresses standards-based semantic interoperability for secondary use of EHRs in pharmacovigilance domain. She has acted as the technical co-chair of the IHE QRPH domain, which is a standardization initiative in the field of quality reporting, secondary use of EHRs for research purposes and public health domain. She has published more than 90 papers in refereed international conferences and journals. Currently she is acting as the R&D director of SRDC A.Ş.

Abstract:
In this talk, Dr. Ertürkmen will present the challenges of chronic disease management, and will present an ICT infrastructure facilitating integrated care enabling multi-disciplinary care team members to collaboratively manage the care of chronic disease patients. The underlying interoperability architecture and clinical decision support systems will be introduced. Two example systems from Europe and Turkey will be presented.