Dr. Arzucan Özgür will give a seminar on “Discovering Molecular Interactions using Language Processing Techniques” on 25 December at 4 pm. The abstract of the talk and a short bio is shared below.

Bio:
Arzucan Özgür is an associate professor at the Computer Engineering Department of Boğaziçi University. She holds a Ph.D. degree in Computer Science and Engineering from the University of Michigan, and MS and BS degrees from the Department of Computer Engineering at Boğaziçi University. She is a recipient of the FP7 Marie Curie Career Integration Grant as well as The Science Academy Young Scientist Award (BAGEP 2016) and the Turkish Science Academy Young Scientist Award (TUBA-GEBIP 2019). She is the co-founder of the Text Analytics and Bioinformatics (TABI) Research Lab at Boğaziçi University and a member of the AILAB. Her research areas are in the intersection of Bioinformatics and Natural Language Processing. Her recent focus has been on developing language processing techniques for information extraction and knowledge discovery from textual data available in natural language or in biology.

Abstract:
New discoveries are often disseminated through scientific publications. Due to the huge and rapidly growing scientific literature in life sciences, most of the important information remains hidden in the unstructured text of the published papers. Automatically extracting the useful information using natural language processing techniques and presenting the extracted information to the scientists in a structured format is vital for facilitating research in this domain. In the first part of her talk, Dr. Arzucan Özgür will describe Vapur, a search system that they developed for enabling easy access to protein-chemical compound relations in the Covid-19 related literature. Vapur’s pipeline includes sentence segmentation, named entity recognition and normalization, relation extraction, query correction, and similar molecule suggestion components. Users can search using protein or compound names and related molecules as well as the sentences in the publications describing these relations are displayed as a result. Evaluations by domain experts revealed that Vapur may be useful for supporting drug and vaccine development studies in this area.

Besides the text written in natural language, Dr. Arzucan Özgür and her team hypothesize that molecules are also written in a certain molecular language. For example, DNA can be considered as written in a language with an alphabet of four letters. Similarly, proteins and chemical compounds can also be represented in textual format. In the second part of her talk, she will describe ChemBoost, a method for predicting the binding affinities of protein-chemical compound interactions. ChemBoost assumes that molecules are documents made up of words and uses language processing methods to represent them. Since ChemBoost is only based on the textual representations of the molecules, it can be used when their three-dimensional structures are not known. The team believes that language processing-based approaches can be effective in revealing the interactions between molecules and may shed light on new drug/vaccine development studies.

Dr. Uygar Sümbül will give a seminar on “Multimodal characterization of neuronal cell types” on December 4 at 10 am. The abstract of the talk and a short bio is shared below.

Bio:
Uygar Sümbül is an Assistant Investigator at Allen Institute, Seattle, where he focuses on the intersection of neuroscience and machine learning. He obtained a Ph.D. in Electrical Engineering and a Ph.D. minor in Mathematics from Stanford University in 2009. Prior to joining the Allen Institute, Uygar held postdoctoral researcher positions at the Dept. of Brain and Cognitive Sciences at MIT, and the Dept. of Statistics & Grossman Center for the Statistics of Mind at Columbia University. He completed a BS degree in Electrical Engineering at Bilkent University, Turkey, in 2003.

Abstract:
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While paired multimodal measurements to simultaneously characterize single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. We present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction and identifies cell types that are consistent across modalities.

Dr. İnci M. Baytaş will give a seminar on “Data-Driven Techniques in Biomedical Informatics” on November 27 at 4 pm. The abstract of the talk and a short bio is shared below.

Bio:
İnci M. Baytaş received her bachelor's and master's degrees from Electronics and Communication Engineering Department at Istanbul Technical University in 2012 and 2014, respectively. She joined the Ph.D. program of Computer Science and Engineering at Michigan State University in 2014. She has been working as an Assistant Professor in the Computer Engineering Department at Bogazici University since 2019. Her research interests are deep learning, adversarial machine learning, multi-task learning, temporal analysis, and their applications on biomedical informatics.

Abstract:
With innovations in digital data acquisition devices and increased memory capacity, virtually all commercial and scientific domains have been witnessing exponential growth in the amount of data they can collect. For instance, healthcare is experiencing tremendous growth in digital patient information due to the high adaptation rate of electronic health record systems in hospitals. The abundance of data offers many opportunities to develop robust and versatile algorithms. Biomedical informatics is an interdisciplinary domain, where machine learning techniques are adopted to analyze electronic health records (EHRs). EHR comprises digital patient data with various modalities and depicts an instance of big data. For this reason, analysis of digital patient data is quite challenging although it provides a rich source for clinical research. In this seminar, common tasks and challenges in biomedical informatics will be introduced. Then, state-of-the-art machine learning and deep learning solutions to some of the prevalent biomedical informatics tasks will be presented.

Dr. Sanjarbek Hudaiberdiev will give a seminar on “Using Deep Learning to infer causality of GWAS SNPs” on November 20 at 4 pm. The abstract of the talk and a short bio is shared below.

Bio:
Sanjar Hudaiberdiev is a research fellow at National Center for Biotechnology and Information (NCBI) of National Institutes of Health (NIH). His current research is focused on the development and implementation of data-centric computational approaches to understand the mechanisms of non-coding regulatory elements in human genome. He received his BSc degree in Computer Science at Erciyes University in Kayseri, and Ph.D. degree at International Center for Genetic Engineering and Biotechnology (ICGEB) in Trieste, Italy.

Abstract:
Decomposition of GWAS signals into the causal SNPs and the noise brought by linkage disequilibrium (LD) remains a key question to understand the mechanisms and the genetic underpinnings of complex human diseases. The overwhelming majority of SNPs linked to the diseases fall into non-coding regions of human genome. We developed a two-step deep learning (DL) framework that identifies active regions within regulatory elements (REs) and quantifies the influence of any arbitrary point mutation on the activity of the host RE. Our framework is tissue-specific, and we show that the mutation-susceptible regions in REs largely correspond to the binding sites of active transcription factors (TFs) and that the predicted mutational impact of these regions matches the binding specificity of the corresponding TFs. We further show that our scores of mutational impacts strongly correlate with the experimental data from a set of arrays, including those for quantitative trait loci for chromatin accessibility (caQTLs), massively parallel reporter assays (MPRAs), and reporter assay QTLs (raQTLs). Application of our method for resolving the ambiguity in LD of Type 2 Diabetes (T2D) genome-wide association studies (GWASs) consisting of 403 strong genetic T2D associations resulted in 706 (5.7%) genetic associations with quantifiable impact on pancreatic islet enhancer activity. We confirmed the directionality and magnitude of disrupted enhancer activity for a panel of experimentally validated T2D single nucleotide polymorphisms (SNPs), and we predicted 46 novel T2D causative enhancer mutations.

Dr. Burçak Otlu will give a seminar on “Evaluating topography of mutational signatures with SigProfilerTopography” on November 13 at 4 pm. The abstract of the talk and a short bio of Dr. Otlu are provided below.

Bio:
Burçak Otlu is a postdoctoral scholar in UC San Diego, Cellular and Molecular Medicine Department since March 2018. She is working with Asst. Prof. Ludmil Alexandrov on mutational signatures and topography of somatic mutations across the genome. Meanwhile, she is one of the scientists in CRUK Grand Challenge, Mutographs of Cancer Project. Formerly, she was a visiting scholar in UC San Diego, Computer Science, and Engineering Department under the supervision of Prof. Vineet Bafna between August 2017 and February 2018. She is the author of papers published in Bioinformatics, BMC Bioinformatics, Circulation, Nature Communications, and Cell. She received her Ph.D. from Middle East Technical University, Computer Engineering Department where she focused on developing tools and techniques for assessing the functional relevance of genomic loci. Prior to coming to UC San Diego, she has worked as a teaching assistant in the same department for several years. She has also worked in the private sector as a software engineer.

Abstract:
Mutations are found on the genomes of all cancerous and normal somatic cells. These mutations were generated by the activities of endogenous and exogenous mutational processes which were operative throughout the cell lineage. Mutational processes imprint characteristic patterns of somatic mutations, termed, mutational signatures. To exemplify, mutational signature associated with tobacco smoking causes C>A mutations prominently on the transcribed strand, whereas mutational signature due to exposure to ultraviolet light results in C>T mutations on the untranscribed strand during DNA transcription. Previous analyses have demonstrated that somatic mutations in cancer are not uniformly distributed across the landscape of the genome. Importantly, mutational signatures imprinted by different mutational processes exhibit distinct topographical properties including to be located on (i) early or late replicating regions, (ii) genic or intergenic regions, (iii) transcribed strand or untranscribed strand of DNA with respect to the transcription process, (iv) leading or lagging strand of DNA in regard to DNA replication. Remarkably, mutational signatures may accumulate somatic mutations preferably at nucleosome occupied loci, chromatin accessible regions, transcription factor binding sites, and histone modification sites. Here I present SigProfilerTopography, the most advanced tool for evaluating the topography of mutational signatures. SigProfilerTopography allows examining all types of mutational signatures and reveals topographical dependencies related to chromatin accessibility, nucleosome occupancy, histone modifications, transcription factor binding sites, replication timing, transcription strand bias, replication strand bias, and processivity. The tool also allows performing user-defined custom analysis based on custom assays. Having augmented with realistic simulated mutations, SigProfilerTopography assesses the significance of its findings which finally characterizes the mutational signatures and gives insight about their underlying biological mechanisms.

Dr. Melike Dönertaş will give a seminar on “In silico studies to understand and intervene in aging” on November 6 at 4 pm. The abstract of her talk and her short bio is shared below.

Bio:
Melike Dönertaş completed her BSc in Molecular Biology and Genetics in 2014 and her MSc in Biological Sciences in 2016, both at METU. She worked as a research and teaching assistant during her MSc. Then she did her Ph.D. at the University of Cambridge as an EMBL fellow. She is currently a postdoctoral fellow at the EMBL-EBI and Institute of Healthy Ageing at UCL. She has worked on a variety of topics ranging from cheminformatics to evolutionary genomics and ancient DNA studies, but her main research focus has been on aging, disease, and anti-aging interventions.

Abstract:
Aging is the major risk factor for a variety of non-communicable diseases. With the increase in life expectancy, aging poses significant challenges to individuals, societies, and healthcare systems. Aging is a complex phenotype with many interconnected cellular and organismal phenotypes. Thus, understanding aging and finding potential interventions require system-level approaches. In this seminar, Melike Dönertaş will summarise her recent research to understand the link between aging and age-related diseases, and different drug repurposing strategies to find potential anti-aging interventions for humans. She will first describe the common genetics of age-related diseases determined by the analysis of medical and genomic data for almost half a million participants in the UK. She will also present her ongoing research to find drugs that can promote healthy aging in humans using transcriptome, structural data, and electronic health records.

Dr. Igor Mapelli will give a seminar on “Archaeological Data Analysis in the Era of Big Data” on October 30 at 4 pm. The abstract of his talk and his short bio is shared below.

Bio:
Igor Mapelli is a postdoctoral researcher at the Biology Department of the Middle East Technical University (METU). Igor completed his Ph.D. studies in medical informatics at METU in early 2019 during the course of which he worked on the electrophysiology of the human brain specializing in visual working memory. Before moving to Turkey, he earned his MSc in computer science from the University of Turin in Italy where he had concentrated majorly on data mining and artificial intelligence. His research interests lie in the areas of neural oscillations and machine learning, ranging from more theoretical aspects of human electrophysiology to practical applications such as brain-computer interfaces. He has collaborated actively on projects in few other disciplines which contributed to consolidating his data analysis expertise. Currently, in his postdoctoral position, he is applying machine learning and network analysis techniques on archaeological and biological data to help studying the evolution of mobile hunter-gatherer societies into sedentary village farmers.

Abstract:
Archaeological Data Analysis in the Era of Big Data The rapid growth of available data across the whole scientific disciplines opened novel opportunities with regard to both research questions and analytical methodologies that rely on machine learning for data-driven discovery. In this respect, the field of archaeology is no exception and in addition to the rise in the quantity of material culture and historical data, the heterogeneity of data is also increasing, thanks to the contribution from fields such as climate science, genetics, and medicine. Within this context, we will examine how statistical coefficients of relatedness (i.e., kinship coefficient θ and Cotterman coefficients k0, k1, k2) aiming at the characterization of genetic kinship levels among ancient individuals cannot reliably evaluate all cases. Thus, alternative approaches, from classical machine learning techniques to more recent deep learning approaches, are needed. Furthermore, we will present how network analyses – based on Kulczynski-2 similarity measures – may be applied over material culture data to study properties and relationships between geographical sites and across different time periods.

Hacettepe Üniversitesi Tıp Fakültesi, İnfeksiyon Hastalıkları ve Klinik Mikrobiyoloji Anabilim Dalı’ndan  Uzman Dr. Ahmet Görkem ER’in 2019 Koronavirüs Salgını güncel durumu hakkında elde edilen bilgileri ve son değerlendirmeleri paylaşacağı konuşma 21 Şubat Cuma günü 13:00-14:00 saatleri arasında ODTÜ Enformatik Enstitüsü Konferans Odası -1 de yapılacaktır.

Seminer tüm enstitü çalışanları, öğrencileri ve diğer ilgililere açıktır, 

2019 Koronavirüs Salgını – Anlık Durum ve İlk İzlenimler

2019 koronavirüs salgını, SARS ve MERS salgınları sonrası 21. yüzyıldaki üçüncü koronavirüs salgını olarak dikkatleri üzerine çekmiştir. Salgının başlangıcında 2019-nCoV olarak isimlendirilen virüsün Huanan Deniz Ürünleri Pazarı kaynaklı olduğu düşünülmektedir. 08.02.2020 tarihi itibariyle küresel ölçekte doğrulanmış 34.886 olgu ve 724 ölüm görülmüştür, önümüzdeki günlerde de bu sayıların artacağı öngörülmektedir. Oluşturulan çeşitli modellerle virüsün yayılım dinamikleri araştırılmakta, inkübasyon süresi ve temel çoğalma sayısı (R0) gibi salgın parametreleri aydınlatılmaya çalışılmaktadır. Türkiye’de henüz kanıtlanmış olgu görülmemiştir ve T.C. Sağlık Bakanlığının koordinasyonuyla salgına yönelik yaklaşım ve yönetim algoritmaları geliştirilmiş ve uygun korunma önlemleri belirlenmiştir.

2019 Coronavirus Outbreak – Current Situation and First Impressions

2019 coronavirus outbreak attracts the attention as the third coronavirus outbreak following the SARS and MERS outbreaks in the 21st century. The virus, which was named as 2019-nCoV at the beginning of the outbreak, is thought to originate from Huanan Seafood Wholesale Market. As of 02.08.2020, there have been 34.886 confirmed cases and 724 deaths globally, and these numbers are expected to increase in the coming days. The dynamics of virus spread are investigated with various models and outbreak parameters such as incubation time and basic reproduction number (R0) is tried to be illuminated. Yet, there has been no proven cases in Turkey and under the coordination of Republic of Turkey Minister of Health, algorithms for the detection and management of the outbreak have been developed and appropriate prevention measures have been determined.