Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application

Abstract Background Genotype-phenotype association has been one of the long-standing problems in bioinformatics. Identifying both the marginal and epistatic effects among genetic markers, such as Single Nucleotide Polymorphisms (SNPs), has been extensively integrated in Genome-Wide Association Studies (GWAS) to help derive “causal” genetic risk factors and their interactions, which play critical roles in life and disease systems. Identifying “synergistic” interactions with respect to the outcome of interest can help accurate phenotypic prediction and understand the underlying mechanism of system behavior. Many statistical measures for estimating synergistic interactions have been proposed in the literature for such a purpose. However, except for empirical performance, there is still no theoretical analysis on the power and limitation of these synergistic interaction measures. Results In this paper, it is shown that the existing information-theoretic multivariate synergy depends on a small subset of the interaction parameters in the model, sometimes on only one interaction parameter. In addition, an adjusted version of multivariate synergy is proposed as a new measure to estimate the interactive effects, with experiments conducted over both simulated data sets and a real-world GWAS data set to show the effectiveness. Conclusions We provide rigorous theoretical analysis and empirical evidence on why the information-theoretic multivariate synergy helps with identifying genetic risk factors via synergistic interactions. We further establish the rigorous sample complexity analysis on detecting interactive effects, confirmed by both simulated and real-world data sets.https://deepblue.lib.umich.edu/bitstream/2027.42/142802/1/12864_2018_Article_4552.pd

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Biomedical Information Extraction: Mining Disease Associated Genes from Literature

Disease associated gene discovery is a critical step to realize the future of personalized medicine. However empirical and clinical validation of disease associated genes are time consuming and expensive. In silico discovery of disease associated genes from literature is therefore becoming the first essential step for biomarker discovery to support hypothesis formulation and decision making. Completion of human genome project and advent of high-throughput technology have produced tremendous amount of data, which results in exponential growing of biomedical knowledge deposited in literature database. The sheer quantity of unexplored information causes information overflow for biomedical researchers, and poses big challenge for informatics researchers to address user's information extraction needs. This thesis focused on mining disease associated genes from PubMed literature database using machine learning and graph theory based information extraction (IE) methods. Mining disease associated genes is not trivial and requires pipelines of information extraction steps and methods. Beginning from named entity recognition (NER), the author introduced semantic concept type into feature space for conditional random fields machine learning and demonstrated the effectiveness of the concept feature for disease NER. The effects of domain specific POS tagging, domain specific dictionaries, and named entity encoding scheme on NER performance were also explored. Experimental results show that by combining knowledge base with concept feature space, it can significantly improve the overall disease NER performance. It has also shown that shallow linguistic features of global and local word sequence context can be used with string kernel based supporting vector machine (SVM) for efficient disease-gene relation extraction. Lastly, the disease-associated gene network was constructed by utilizing concept co-occurrence matrix computed from disease focused document collection, and subjected to systematic topology analysis. The gene network was then merged with a seed-gene expanded network to form heterogeneous disease-gene network. The author identified and prioritized disease-associated genes by graph centrality measurements. This novel approach provides a new mean for disease associated gene extraction from large corpora.Ph.D., Information Studies -- Drexel University, 201

Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

Since the history of humans there was no definitive cure for cancer. The rapid development in the field of bioinformatics has resulted in acceleration of advancement of cancer research. As computing and IT technology improves over time the use and importance of bioinformatics will also rise. The bulk of biological data created by biomedical researchers has increased over the years, and it has become difficult to store and analyze that data. Faster computer processors and advancement in quantum computing will solve the conventional problem of slow data processing and will make the use of bioinformatics even attractive for scientists and researchers across the globe. The success of potential drug candidates and vaccines were identified and credit goes to bioinformatics gene simulation sequencing, simulation and fast data processing. The results were development of a vaccine in record time all thanks to bioinformatics approaches. This paper explores the contribution that bioinformatics has been able to make in the field of ovarian cancer and how the use of DNA sequencing and simulation helped in developing targeted drugs such as PARP inhibitors. It also elucidates the impact bioinformatics can make in developing effective therapies in times to come. Genome sequencing has paved the way in understanding the disease, possible treatment options analyze mutations and further predict the drug target. In this review we will highlight different aspects of bioinformatics tools and techniques that have accelerated the ovarian cancer research

Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well

MICA: microRNA integration for active module discovery

A successful method to address disease-specific module discovery is the integration of the gene expression data with the protein-protein interaction~(PPI) network. Although many algorithms have been developed for this purpose, they focus only on the network genes~(mostly on the well-connected ones); totally neglecting the genes whose interactions are partially or totally not known. In addition, they only make use of the gene expression data which does not give the complete picture about the actual protein expression levels. The cell uses different mechanisms, such as microRNAs, to post-transcriptionally regulate the proteins without affecting the corresponding genes' expressions. Due to this complexity, using a single data type is definitely not the correct way to find the correct module(s). Today, the unprecedented amount of publicly available disease-related heterogeneous data encourages the development of new methodologies to better understand complex diseases. In this work, we propose a novel workflow Mica, which, to the best of our knowledge, is the first study integrating miRNA, mRNA, and PPI information to identify disease-specific gene modules. The novelty of the Mica lies in many directions, such as the early modification of mRNA expression with microRNA to better highlight the indirect dependencies between the genes. We applied Mica on microRNA-Seq and mRNA-Seq data sets of $699$ invasive ductal carcinoma samples and $150$ invasive lobular carcinoma samples from the Cancer Genome Atlas Project~(TCGA). The Mica modules are shown to unravel new and interesting dependencies between the genes. Additionally, the modules accurately differentiate between the case and control samples while being highly enriched with disease-specific pathways and genes