Graduate School of Informatics

In this study, we propose to integrate large-scale gene/protein annotation data by using non-negative matrix factorization (NMF). Using NMF, the ultimate aim here is to predict the unknown binary relationships between these biological entities; and to represent these entities (i.e., proteins, functions and disease entries) as informative and non-redundant quantitative feature vectors (using the low-rank feature matrices generated by the factorization process), which can be used in diverse data mining and machine learning tasks in the future, such as the automated annotations of proteins or the construction of biological knowledge graphs.

Date: 30.01.2020 / 15:30 Place: A-212

Studies showed that single nucleotide variations that alter the protein sequence, structure and function are associated with many diseases in humans. However, the current rate of manually annotating reported nsSNPs cannot catch up with the rate of producing new sequencing data. To aid this process, automated computational approaches are being developed and applied on the unknown data. In this study, we propose a new methodology to collect and organize the information related to the effects of nsSNPs at the amino acid sequence level from various biological databases and to utilize this information in a supervised machine-learning based system to predict the function disrupting capacities of mutations with unknown consequences.

Date: 30.01.2020 / 14:00 Place: A-212

Sorafenib is one of FDA approved targeted agents in HCC treatment. PI3K/AKT pathway is altered in 50% of hepatoma, hence understanding how Sorafenib and PI3K/AKT pathway inhibitors act at signalling level is crucial for targeted therapies and to reveal their off-target effects. In this work, we use gene expression profiles of HCC cells treated with seven different drugs/inhibitors and combination. Our aim is to reveal the important targets and modulators in a drug treatment by inferring the dysregulation of Interactome. In other words, we search for the mechanism of action of drugs in a network context beyond gene list.

Date: 13.01.2020 / 14:00 Place: A-108

Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors was developed. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2015 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, VDTW was tested with corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data.

Date: 15.01.2020 / 14:00 Place: Conference Hall-1

This thesis consists of three eye tracking applications. Main purpose of the applications is to develop a communication system for users by tracking eye movements.

Date: 14.01.2020 / 16:00 Place: A-212

Face-to-face conversation implies that interaction should be characterized as an inherently multimodal phenomenon involving both verbal and nonverbal signals. Gaze is a nonverbal cue that plays a key role in achieving social goals during the course of conversation. The purpose of this study is twofold: (i)to examine gaze behavior (i.e., aversion and gaze on face) and relations between gaze and speech in face to face interaction, (ii)to construct computational models to predict gaze behavior using high-level speech features.

Date: 13.01.2020 / 10:30 Place: Conference Hall-1