Bioinformatics Commons^™

Computational Identification Of Noncoding Driver Mutations Based On Impact On Rna Processing, Kevin Zhu

Dissertations & Theses (Open Access)

Despite the prevalence of mutations in the noncoding regions of the DNA, their effects on cancer development remain largely uninvestigated. This is especially evident when compared to coding mutations, which have been relatively well-studied and, in certain cases, been identified as driver mutations for cancer. Recent studies, however, have identified noncoding mutations that frequently appear in certain types of cancer, which may be evidence that those mutations are important to cancer development. Nonetheless, the role of noncoding mutations in cancer remains unclear. A potential vector for understanding this mechanism is through observing the relation between noncoding mutations and functional RNA …

Mutations In Braf Are Associated With Higher Levels Of Immune Infiltrates In Microsatellite-Stable Colon Cancer, Jake Rubin, Eduard Porta Parto

GW Research Days 2016 - 2020

While BRAF is among the most well-established oncogenes in human cancers, more recently it has garnered attention for its role in suppressing antitumor immunity, especially in melanoma. Because tumor-infiltrating lymphocyte (TIL) density is strongly prognostic in colorectal cancer (CRC)⁷, we decided to investigate the connection between TIL density and the BRAF-activating V600E mutation in CRC.

We used ESTIMATE to quantify immune infiltrate in samples from the TCGA colon adenocarcinoma (COAD) dataset (n = 216). This is an algorithm that uses the gene-expression signature of 141 immune-related genes to infer the presence of immune cells in the tumor infiltrate. …

A Novel Approach For Classifying Gene Expression Data Using Topic Modeling, Soon Jye Kho, Himi Yalamanchili, Michael L. Raymer, Amit Sheth

Kno.e.sis Publications

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA inclustering as well as in classification of cancer and healthy tissues using lung cancer …