Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
Browse
32 results
Search Results
Publication Restricted A data-centric approach for investigation of protein-protein interfaces in protein data bank(Koç University, 2021) Abalı, Zeynep; Keskin, Özlem; 0000-0002-4202-4049; Koç University Graduate School of Sciences and Engineering; Data Science; 26605
Publication Metadata only A kernel-based multilayer perceptron framework to identify pathways related to cancer stages(Springer Science and Business Media Deutschland GmbH, 2023) Mokhtaridoost, Milad; N/A; Department of Industrial Engineering; Soleimanpoor, Marzieh; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Standard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of pathways plays a crucial role in the growth and progression of cancer from early-stage to late-stage. That is why we implemented a kernel-based neural network framework that integrates pathways and gene expression data using multiple kernels and discriminates early- and late-stages of cancers. Our goal is to identify the relevant molecular mechanisms of the biological processes which might be driving cancer progression. As the input of developed multilayer perceptron (MLP), we constructed kernel matrices on multiple views of expression profiles of primary tumors extracted from pathways. We used Hallmark and Pathway Interaction Database (PID) datasets to restrict the search area to interpretable solutions. We applied our algorithm to 12 cancer cohorts from the Cancer Genome Atlas (TCGA), including more than 5100 primary tumors. The results showed that our algorithm could extract meaningful and disease-specific mechanisms of cancers. We tested the predictive performance of our MLP algorithm and compared it against three existing classification algorithms, namely, random forests, support vector machines, and multiple kernel learning. Our MLP method obtained better or comparable predictive performance against these algorithms.Publication Restricted An information theoretical study on nanoscale communication channels with molecule diversity(Koç University, 2013) Ünlütürk, Bige Deniz; Akan, Özgür Barış; 0000-0003-2523-3858; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 6647Publication Restricted Analysis of human protein-protein interaction network and cancer proteins using structural information(Koç University, 2008) Kar, Gözde; Keskin, Özlem; 0000-0002-4202-4049; Koç University Graduate School of Sciences and Engineering; Electrical and Computer Engineering; 26605Publication Open Access Artificial intelligence approaches to human-microbiome protein-protein interactions(Elsevier, 2022) Lim, Hansaim; Tsai, Chung-Jung; Nussinov, Ruth; Department of Computer Engineering; Department of Chemical and Biological Engineering; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; Graduate School of Sciences and Engineering; 8745; 26605; N/AHost-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein–protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.Publication Restricted Communication theoretical modeling and analysis of neuronal communication with synaptic plasticity(Koç University, 2022) Khan, Tooba; Akan, Özgür Barış; 0000-0003-2523-3858; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 6647Publication Metadata only Computational analysis of the binding free energy of H3K9ME3 peptide to the tandem tudor domains of JMJD2A(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Erman, Burak; Özboyacı, Musa; Faculty Member; Faculty Member; Faculty Member; PhD Student; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; 179997; N/AJMJD2A is a histone lysine demethylase enzyme which plays a prominent role in the development of prostate and esophageal squamous cancers. Consisting of a JmjC, a JmjN, two PHD and two tandem tudor domains, JMJD2A recognizes and binds to four different methylated histone peptides: H3K4me3, H4K20me3, H4K20me2 and H3K9me3, via its tudor domains. Of the four histone peptides, only recognition of the H3K4me3 and H4K20me3 by JMJD2A-tudor has been identified. In this study, we investigated the recognition of trimethylated H3K9 by the tandem tudor domains of JMJD2A. Using the molecular dynamics simulations, we performed normal mode and molecular mechanics - Poisson Boltzmann / generalized born - surface area (MM-PB/GB-SA) analysis to find the entropic and enthalpic contributions to binding free energy respectively. We showed that binding of the ligand is mainly driven by favorable van der Waals interactions made after complexation. Our findings suggest that, upon complex formation, H3K9me3 peptide adopts a similar binding mode and the same orientation with H3K4me3 peptide.Publication Restricted Computational search of the interaction between melanopsin & cryptochrome proteins(Koç University, 2006) Ünal, Evrim Besray; Erman, Burak; Kavaklı, İbrahim Halil; 0000-0002-2496-6059; 0000-0001-6624-3505; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997; 40319Publication Metadata only Coupling between energy and residue position fluctuations in native proteins(IEEE, 2010) Department of Chemical and Biological Engineering; N/A; Erman, Burak; Gür, Mert; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179997; 216930The coupling between energy fluctuations and positional fluctuations in molecular dynamics trajectories of Crambin at 310 K is studied. Coupling with energy fluctuation is evaluated for both atomic positions and residue positions. Couplings show values which fluctuate around the previously proposed theoretical value under harmonic approximation. The magnitude of these correlations is in agreement, on the average, with the harmonic approximation. Additionally coupling between energy fluctuations and atom-atom distance fluctuations are evaluated. This coupling indicates how much each interaction among different atoms/residues is correlated with the protein's total energy fluctuations. Some atom's/residue's interactions have shown outstanding correlation. Moreover coupling of residue fluctuations between different modes is studied. © 2009 IEEE.Publication Restricted Critical assessment of protein-protein interaction databases and features towards prediction of interactions(Koç University, 2009) Ulubaş, Mehmet Cengiz; Gürsoy, Attila; 0000-0002-2297-2113; Koç University Graduate School of Sciences and Engineering; Electrical and Computer Engineering; 8745