Biology subjects

Bozdag, S.

Publications and source records attributed to Bozdag, S..

3 recordsLinked to original sources

bioRxiv2019

miRDriver: A Tool to Infer Copy Number Derived miRNA-Gene Networks in Cancer

Copy number aberration events such as amplifications and deletions in chromosomal regions are prevalent in cancer patients. Frequently aberrated copy number regions include regulators such as microRNAs (miRNAs), which regulate downstream target genes that involve in the important biological processes in tumorigenesis and proliferation. Many previous studies explored the miRNA-gene interaction networks but copy number-derived miRNA regulations are limited. Identifying copy number-derived miRNA-target gene regulatory interactions in cancer could shed some light on biological mechanisms in tumor initiation and progression. In the present study, we developed a computational pipeline, called miRDriver which is based on the hypothesis that copy number data from cancer patients can be utilized to discover driver miRNAs of cancer. miRDriver integrates copy number aberration, DNA methylation, gene and miRNA expression datasets to compute copy number-derived miRNA-gene interactions in cancer. We tested miRDriver on breast cancer and ovarian cancer data from the Cancer Genome Atlas (TCGA) database. miRDriver discovered some of the known miRNAs, such as miR-125b, mir-320d, let-7g, and miR-21, which are known to be in copy number aberrated regions in breast cancer. We also discovered some potentially novel miRNA-gene interactions. Also, several miRNAs such as miR-127, miR-139 and let-7b were found to be associated with tumor survival and progression based on Cox proportional hazard model. We compared the enrichment of known miRNA-gene interactions computed by miRDriver with the enrichment of interactions computed by the state-of-the-art methods and miRDriver outperformed all the other methods.\n\nCCS CONCEPTSO_LIBioinformatics\nC_LIO_LIComputational Genomics\nC_LIO_LIBiological Networks\nC_LI

bioinformatics↗

bioRxiv2019

PhenoGeneRanker: A Tool for Gene Prioritization Using Complete Multiplex Heterogeneous Networks

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally-related genes tend to be close to each other in the biological networks.\n\nIn this study, we present PhenoGeneRanker, an improved version of a recently developed network propagation method called Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH). PhenoGeneRanker allows multi-layer gene and disease networks. It also calculates empirical p-values of gene ranking using random stratified sampling of genes based on their connectivity degree in the network.\n\nWe ran PhenoGeneRanker using multi-omics datasets of rice to effectively prioritize the cold tolerance-related genes. We observed that top genes selected by PhenoGeneRanker were enriched in cold tolerance-related Gene Ontology (GO) terms whereas bottom ranked genes were enriched in general GO terms only. We also observed that top-ranked genes exhibited significant p-values suggesting that their rankings were independent of their degree in the network.\n\nCCS CONCEPTS* Bioinformatics * Biological networks * System biology * Computational genomics\n\nAvailability and implementationThe source code is available on GitHub at https://github.com/bozdaglab/PhenoGeneRanker under Creative Commons Attribution 4.0 license\n\nContactcdursun@mcw.edu or serdar.bozdag@marquette.edu

bioinformatics↗

bioRxiv2019

HAVE IT, KNOW IT, BUT DO NOT SHOW IT: EXAMINING PHYSIOLOGICAL AROUSAL, ANXIETY, AND FACIAL EXPRESSIONS OVER THE COURSE OF A SOCIAL SKILLS INTERVENTION FOR AUTISTIC ADOLESCENTS

Facial expressions provide a nonverbal mechanism for social communication, a core challenge for autistic people. Little is known regarding the association between arousal, self-report of anxiety, and facial expressions among autistic adolescents. Therefore, this study investigated session-by-session facial expressions, self-report of anxiety, and physiological arousal via Electrodermal Activity (EDA), of 12 autistic male adolescents in a didactic social skills intervention setting. The goals of this study were threefold: 1) identify physiological arousal levels (\"have-it\"), 2) examine if autistic adolescents facial expressions indicated arousal (\"show-it\"), and 3) determine whether autistic adolescents were self-aware of their anxiety (\"know-it\"). Our results showed that autistic adolescents self-rated anxiety was significantly associated with peaks in EDA. Both machine learning algorithms and human participant-based methods, however, had low accuracy in predicting autistic adolescents arousal state from facial expressions, suggesting that autistic adolescents facial expressions did not coincide with their arousal. Implications for understanding social communication difficulties among autistic adolescents, as well as future targets for intervention, are discussed. This project is registered with ClinicalTrials.gov, Identifier: NCT02680015.

bioinformatics↗