Gökçen Yılmaz, Ph.D. graduate of Information Systems Department, has been awarded “Prof. Dr. Mustafa Parlar Education and Reasearch Foundation, METU Thesis of the Year Award”.
Topic: Algorithmic Therapeutic Oncology
Speaker: Dr.Murat Çobanoğlu ( UT Southwestern, Lyda Hill Department of Bioinformatics, Texas, USA )
Organized by: Health Informatics Department
Date: 24 December 2018, Monday at 11:30
Place: Graduate School of Informatics Class-01
Abstract: The cost of discovering a new drug has doubled every 9 years to reach more than $2bn per new molecular entity today. The high cost reflects the high rate of failure: only about 10% of all drug candidates in clinical trials get approved. Oncology has the worst success rate among all areas, standing at only 5.1%. Consequently, most new cancer drugs cost in excess of $100k per year per patient, making access to therapy difficult. We want to change that. We think that the solution lies in using clinically relevant assays to replace the reductionist high throughput preclinical assays. The lower throughput of more advanced assays necessitates three or four orders of magnitude reduction in the number of experiments. We propose that large scale public data and machine learning are necessary and sufficient to deliver the required efficiency gain. I will discuss our preliminary results in (i) single cell RNA-seq classification, and clustering using Bayesian Pólya discriminant analysis and Pólya mixture models, (ii) differential TF activity inference using graph constrained fused lasso regularized linear models, (iii) cancer lineage-specific vulnerability identification and exploitation. I will also present one early success story in the form of a drug candidate that specifically kills BRAF WT melanoma, and not normal skin cells or normal immune cells. Drug name and chemical structure will be confidential.
Bio: Murat Can Cobanoglu studied Computer Science & Engineering (major) and Mathematics (minor) in Sabancı University from 2004 to 2008. He then obtained a master’s degree in Computer Science and Engineering also in Sabancı University, between 2008 and 2010, where he worked on the classification of GPCRs using family specific motifs. He received his Ph.D. degree from the Carnegie Mellon University – University of Pittsburgh Joint Ph.D. Program in Computational Biology under the supervision of Ivet Bahar, Ph.D. and D. Lans Taylor, Ph.D. where he worked on computational drug-target interaction prediction and applications in alpha1-antitrypsin deficiency and Huntington’s disease. He then joined the UT Southwestern Medical Center Lyda Hill Department of Bioinformatics as the inaugural UT Southwestern Distinguished Fellow. Outside of work, he is proud to be an épéeist as well as a member of and a frequent visitor to the Dallas Museum of Art.
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Cross-organizational process mining aims to extract commonality and differences among the processes that perform the same tasks in different organizations. The results can be used to create and enhance collaboration capabilities among different organizations. However, variabilities across organizations constitute a challenge to deal with. In this study, we propose a framework in order to measure the degree of similarity among the cross-organizational processes and to extract process families as a tree, by adapting sequence alignment technique. In an alignment, matching regions pinpoint a functional inheritance or a major commonality within process behavior for the organizations. Date: 17 December 2018 |
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Title: Symposium on Human-Robot Interaction
Date: 13 December 2018
Place: Ural Akbulut Conference Hall
Graduate School of Informatics /Cognitive Sciences
In partial fulfillment of the requirements for the degree of Master of Borabay Kadirdağ will defend his thesis.
Title: THE EFFECTS OF COGNITIVE REAPPARISAL & EXPRESSIVE SUPPRESSION ON EXAM PERFORMANCE OF UNIVERSITY STUDENTS
Date: 26th November 2018
Time: 11:30 AM
Place: A-212
Thesis Abstract : The main objective of this thesis is to examine the effect of cognitive (re-)appraisal and expressive suppression and on exam performance in Turkish university students. The study is based on the appraisal-tendency framework of Lerner and Keltner (2000) which defines cognitive appraisal as cognitive meaning making that leads to emotions, (Lerner, Li, Valdesolo, & Kassam, 2015). Expressive suppression, on the other hand, is defined as an aspect of emotional regulation where individuals mask their facial giveaways to hide their emotional states (Niedenthal, P. M., Ric, F., & Krauth-Gruber, S. 2006). Based on the cognitive reappraisal and expressive suppression abilities of the students, their affective responses (PANAS) to anxiety before and after watching a video of a stressful scene were measured to find out if there is an effect of the emotional regulation abilities to exam performances of the students. 63 students with medium-level of exam anxiety based on the Test Anxiety Questionnaire (Nist & Diehl, 1990) participated. Results of the emotional regulation abilities suggest that expressive suppression has a significant effect on exam performances. Those students who suppressed the expression of their emotions less could increase their exam scores as measured in two exams at the beginning and end of the term. The responses based on PANAS scores indicate no significant difference of emotional regulation abilities on exam performance. Keywords: emotion, exam anxiety, emotional regulation, cognitive reappraisal, expressive suppression
Assoc. Prof. Dr. Nurcan Tunçbağ has been awarded UNESCO L'OREAL National For Women in Science Award in Life sciences category.
Graduate School of Informatics /Information Systems
In partial fulfillment of the requirements for the degree of Master of Burak Ünaltay will defend his thesis.
Title: A TRANSFORMATIVE EMBEDDED SOFTWARE ARCHITECTURE FOR IMAGE PROCESSING APPLICATIONS
Date: 14th November 2018
Time: 14:00 AM
Place: B-116
Thesis Abstract : Real time image processing is often subjected to very harsh constraints due to the nature of the embedded hardware it is implemented on. Embedded hardware offer very limited memory, has low processing power and required to have low power needs.These constraints however, cannot be an excuse to give up on performance, as many of the applications in this field of work are created for high end military systems, border security, medical applications and areas similar to these where performance is critical. Unfortunately, many of the image processing algorithms used in these systems are rarely developed for the limited boundaries of embedded world. As such, there is a need for a methodology where one can transform image processing algorithms that is written in high level languages (matlab, python etc..) or even in pseudo-code to the low level languages (c++, c, assembly) that is more suited for embedded world. In this study, existing software development processes that are being used for this effort will be reviewed, and an embedded software architecture will be proposed as a solution to the problems stated above.
Graduate School of Informatics /Cognitive Sciences
In partial fulfillment of the requirements for the degree of Master of Dilek Deniz Bilgiç will defend his thesis.
Title: PRESCHOOL CHILDREN’S STORY CONSTRUCTION AND USE OF DEIXIS IN FICTIONAL NARRATIVES
Date: 14th November 2018
Time: 10:00 AM
Place: A-108
Thesis Abstract : The Deictic Shift theory suggests that for interpreting an utterance, deictic terms must be used and one’s deictic center needs to be shifted with respect to the speaker’s. In the present study, Turkish preschool children’s fictional narratives are studied by examining the deictic terms they use to construct the story-world context within the theory of Deictic Shift and Deictic Center. For this goal, narratives elicited by a picture-based book by 47 preschool children between ages 3;6 and 6 are explored and compared to 23 adults’ narratives. Younger children used more demonstrative deictic terms, suggesting that they are tuned to the picture-book rather than the story context. They also used temporal deictics less frequently than adults, indicating that temporal deictics develop alongside the ability of plot organization. Overall, the results show that narrative development goes hand in hand with the development of how the expressions of the real-world context are shifted to the story-world context and the development of deictic expressions to convey psychological proximity.
Title: SEQUENCE ALIGNMENT BASED PROCESS FAMILY EXTRACTION
PhD Candidate: Eren Esgin
Program: Informations Systems Department
Date: 17 December 10:00
Place: Conference Hall-01
Abstract: Business Process Management (BPM) paradigm gains growing attention by generic process design and execution capabilities empowered by process-aware information systems. During execution of these transactional information systems, end-users leave traces in the form of event logs, which can be used as a main data source for end user behavior analysis. Process mining encompasses the techniques for automated process discovery from these event logs, conformance checking between the reference process model and process executions, as well as analyzing, predicting and enhancing the performance of business processes. With the emergence of new shared economical models and system architectures, monolithic process perspective is evolved through cross-organizational applications. While contemporary information systems provide functionality for process management within the organizations, a systematic approach to support and analyze multiorganizational processes is missing. Cross-organizational process mining supports the use of commonality and collaboration for process configuration. However, this functionality surplus creates the challenge of dealing with variability across organizations. In this study, we propose a three phased cross-organizational process mining framework in order to extract the commonalities among different organizations serving the same business values. While dominant behavior extraction phase initially derives the sequence of tasks expressing the most typical behavior within the process instances, sequence alignment phase measures the degree of similarities between the process candidates by confidenceenhanced cost functioning, and depicts the neighborhood among these alternatives in terms of process family tree. At process configuration phase, common regions that indicate a functional inheritance or abstractions in the process families are visualized at sequence alignment matrices and interpreted by new feature sets, namely identical and maximal identical pair. According to the experimental results, proposed approach presents a viable and robust cost function in incorporating the business context at process similarity measurement and clustering the process alternatives into process families.
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