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An integrative analysis of methylation and gene expression is performed using TCGA data for 997 samples from multiple cancer types. We use conditional mutual information to examine each gene separately, identify 798 genes whose expression are repressed by their methylation, and derive gene-specific thresholds to dichotomize their methylation data. For each methylation controlled gene, we evaluate...
We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes,...
ABSTRACT The cancer cell line encyclopedia (CCLE), a joint academic and industry collaboration, provides a vast resource for analyzing the effectiveness of anti-cancer drugs across numerous cell lines. The predictive modeling of tumor sensitivity to targeted drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to targeted drug sensitivity. The...
Data integration is a crucial step in cancer related bioinformatics studies. Bayesian Networks (BNs) is one of the most commonly used methods for integration of multiple data sources. In this paper, we present a modified BN model that can capture and integrate heterogeneous data to increase its predictive performance. The model allows us to infer aberrant networks with scale-free and small world properties,...
We present a statistical framework, hierarchical relevance vector machine (H-RVM), for improved prediction of scalar outcomes using interacting high-dimensional input covariates from different sources. We illustrate our methodology for integrating genomic data from multiple platforms to predict observed clinical phenotypes. H-RVM is a hierarchical Bayesian generalization of the relevance vector machine...
Medulloblastoma is a highly malignant childhood brain tumor and is often characterized by alterations in cell cycle regulatory pathways and genes. Using FDA-approved arsenic trioxide (ATO) treated ND2-SmoAl mouse model, we present an integrated imaging and systems biology approach to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology. Kaplan-Meier survival...
High multiplexing of samples is critical for next generation sequencing (NGS) where within one lane 10s or 100s of samples can be sequenced simultaneously. Often the limited factor is the number of unique DNA sequences that can be used as molecular tags, i.e. barcodes. We propose the creation of a new algorithm to generate these sequences for sample barcoding. Devising algorithms that make the designing...
We consider the design of short barcodes used for parallel sequencing on platforms where substitution errors dominate. A simple randomized method for the construction of barcodes is presented, leading to barcodes with better error-correction capabilities compared to published ones.
ABSTRACT Recent development of next-generation DNA sequencing platforms has dramatically increased the efficiency of sequencing genomes or targeted regions of interest within genomes. Identification of genetic variants is an important downstream application of such platforms. In this paper, we present a novel framework for processing short reads generated by next-generation sequencing platforms, and...
While high-throughput next generation sequencing technologies are rapidly approaching maturity, computational tools for variant calling have significant room for improvement. The recently emerged computational methods typically compare a disease sample directly with its matched control in order to improve the accuracy of variant calling and search disease specific mutations. In this paper, we performed...
A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes...
An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer. Next generation sequencing provides unparalleled ability to probe the expression levels of the entire set of cancer genes and their transcript isoforms. However, there are onerous statistical and computational issues in interpreting...
Any antitumor agent should act very rapidly with high level of efficiency so that it may increase the patient's chance of survival along with a reasonable quality of life during the course of treatment. The goal is to kill as many tumor cells as possible or shift them into a state where they can no longer proliferate. However, biological variabilities among cells in a population and the way they interact...
ABSTRACT In this paper, a new stationary control policy in Boolean networks (BNs) is derived based on the structural properties of basins of attraction (BOA). The new greedy control policy guarantees that no new attractor cycles will be created by intervention to the original BNs. Hence, it will not introduce significant steady-state mass to ambiguous network states, which may represent unknown phenotypes...
ABSTRACT In the last decade, a number of drugs targeting specific proteins have been developed that are becoming common in cancer research as a basis for personalized therapy. How-ever, the numerous aberrations in molecular pathways that can produce cancer necessitate the use of drug combinations as compared to single drugs for treatment of individual cancers. In this article, we consider the design...
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