Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Altered developmental programming of the mouse mammary gland in female offspring following perinatal dietary exposures : a systems-biology perspective.

Mishaps in prenatal development can influence mammary gland development and, ultimately, affect susceptibility to factors that cause breast cancer. This research was based on the underlying hypothesis that maternal dietary composition during pregnancy can alter developmental (fetal) programming of the mammary gland. We used a computational systems-biology approach and Bayesian-based stochastic search variable selection algorithm (SSVS) to identify differentially expressed genes and biological themes and pathways. Postnatal growth trajectories and gene expression in the mammary gland at 10-weeks of age in female mice were investigated following different maternal diet exposures during prenatal-lactational-early-juvenile development. This correlated a decrease in expression of energy pathways with a reciprocal increase in cytokine and inflammatory-signaling pathways. These findings suggest maternal dietary fat exposure significantly influences postnatal growth trajectories, metabolic programming, and signaling networks in the mammary gland of female offspring. In addition, the adipocytokine pathway may be a sensitive trigger to dietary changes and may influence or enhance activation of an immune response, a key event in cancer development

Integrative methods for analysing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES

Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature-mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. This dissertation presents a novel semi-automated framework, named Knowledge Enhanced Data Analysis (KEDA), which incorporates the following components: 1) literature mining of text; 2) classification modeling; and 3) pathway analysis. This framework aids researchers in assigning literature-mining-based prior knowledge values to genes and proteins associated with disease biology. It incorporates prior knowledge into the modeling of experimental datasets, enriching the development process with current findings from the scientific community. New knowledge is presented in the form of lists of known disease-specific biomarkers and their accompanying scores obtained through literature mining of millions of lung and breast cancer abstracts. These scores can subsequently be used as prior knowledge values in Bayesian modeling and pathway analysis. Ranked, newly discovered biomarker-disease-biofluid relationships which identify biomarker specificity across biofluids are presented. A novel method of identifying biomarker relationships is discussed that examines the attributes from the best-performing models. Pathway analysis results from the addition of prior information, ultimately lead to more robust evidence for pathway involvement in diseases of interest based on statistically significant standard measures of impact factor and p-values. The outcome of implementing the KEDA framework is enhanced modeling and pathway analysis findings. Enhanced knowledge discovery analysis leads to new disease-specific entities and relationships that otherwise would not have been identified. Increased disease understanding, as well as identification of biomarkers for disease diagnosis, treatment, or therapy targets should ultimately lead to validation and clinical implementation

Analysis of alterations in the human cancer genome

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.Aneuploidy, an abnormal complement of chromosomes, is present in approximately 90% of human malignancies. Despite over 100 years of research, many questions remain regarding the contribution of aneuploidy to the cancer phenotype. In this thesis, we develop computational methods to infer the presence and specific patterns of aneuploidy across thousands of primary cancer tissue specimens. We then combine these inferences with clinical and genomic features of the cancer samples to refine our understanding of both the clinical implications of aneuploidy, and how it evolves in various human cancers. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with aneuploidy in several cancer types, and which was predictive of poor clinical outcome multiple cancer types. Current genomic characterization techniques measure somatic alterations in a cancer sample in units of genomes (DNA mass). The meaning of such measurements is highly dependent on the tumors purity and its overall ploidy; they are hence complicated to interpret and compare across samples. Ideally, copy-number should be measured in copies-per-cancer-cell. Such measurements are straightforward to interpret and, for alterations that are fixed in the cancer cell population, are simple integer values. We develop two computational methods to infer tumor purity and malignant cell ploidy directly from allelic analysis of DNA. First we describe HAPSEG, a probabilistic method to interpret bi-allelic marker data in cancer samples in order to produce genome-wide estimates of homologue specific copy-ratios. Second, we describe ABSOLUTE, a method that infers purity, ploidy, and absolute copy-numbers from the estimates produced by HAPSEG. In addition, ABSOLUTE can analyze point mutations to detect subclonal heterogeneity and somatic homozygosity. We used ABSOLUTE to analyze ovarian cancer data and discovered that 54% of somatic point mutations were, in fact, subclonal. In contrast, mutations occurring in key tumor suppressor genes, TP53 and NF1 were predominantly clonal and homozygous. Analysis of absolute allelic copy-number profiles from 3,155 cancer specimens revealed that genome-doubling events are common in human cancer, and likely occur in already aneuploid cells in many cancer types. By correlating genome-doubling status with mutation data, we found that homozygous mutations in NF1 occurred predominantly in non-doubled samples. This finding suggests that genome doubling influences the pathways of tumor progression, with recessive inactivation being less common after genome doubling.by Scott L. Carter.Ph.D

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

Unique networks: a method to identity disease-specific regulatory networks from microarray data

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The survival of any organismis determined by the mechanisms triggered in response to the inputs received. Underlying mechanisms are described by graphical networks that can be inferred from different types of data such as microarrays. Deriving robust and reliable networks can be complicated due to the microarray structure of the data characterized by a discrepancy between the number of genes and samples of several orders of magnitude, bias and noise. Researchers overcome this problem by integrating independent data together and deriving the common mechanisms through consensus network analysis. Different conditions generate different inputs to the organism which reacts triggering different mechanisms with similarities and differences. A lot of effort has been spent into identifying the commonalities under different conditions. Highlighting similarities may overshadow the differences which often identify the main characteristics of the triggered mechanisms. In this thesis we introduce the concept of study-specific mechanism. We develop a pipeline to semiautomatically identify study-specific networks called unique-networks through a combination of consensus approach, graphical similarities and network analysis. The main pipeline called UNIP (Unique Networks Identification Pipeline) takes a set of independent studies, builds gene regulatory networks for each of them, calculates an adaptation of the sensitivity measure based on the networks graphical similarities, applies clustering to group the studies who generate the most similar networks into study-clusters and derives the consensus networks. Once each study-cluster is associated with a consensus-network, we identify the links that appear only in the consensus network under consideration but not in the others (unique-connections). Considering the genes involved in the unique-connections we build Bayesian networks to derive the unique-networks. Finally, we exploit the inference tool to calculate each gene prediction-accuracy across all studies to further refine the unique-networks. Biological validation through different software and the literature are explored to validate our method. UNIP is first applied to a set of synthetic data perturbed with different levels of noise to study the performance and verify its reliability. Then, wheat under stress conditions and different types of cancer are explored. Finally, we develop a user-friendly interface to combine the set of studies by using AND and NOT logic operators. Based on the findings, UNIP is a robust and reliable method to analyse large sets of transcriptomic data. It easily detects the main complex relationships between transcriptional expression of genes specific for different conditions and also highlights structures and nodes that could be potential targets for further research

동시조절 유전적 상호작용 발굴을 위한 하이퍼그래프 모델

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2014. 2. 장병탁.A comprehensive understanding of biological systems requires the analysis of higher-order interactions among many genomic factors. Various genomic factors cooperate to affect biological processes including cancer occurrence, progression and metastasis. However, the complexity of genomic interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, this dissertation addresses the problem of analyzing complex relationships among many genomic factors in biological processes including cancers. We propose a hypergraph approach for modeling, learning and extracting: explicitly modeling higher-order genomic interactions, efficiently learning based on evolutionary methods, and effectively extracting biological knowledge from the model. A hypergraph model is a higher-order graphical model explicitly representing complex relationships among many variables from high-dimensional data. This property allows the proposed model to be suitable for the analysis of biological and medical phenomena characterizing higher-order interactions between various genomic factors. This dissertation proposes the advanced hypergraph-based models in terms of the learning methods and the model structures to analyze large-scale biological data focusing on identifying co-regulatory genomic interactions on a genome-wide level. We introduce an evolutionary approach based on information-theoretic criteria into the learning mechanisms for efficiently searching a huge problem space reflecting higher-order interactions between factors. This evolutionary learning is explained from the perspective of a sequential Bayesian sampling framework. Also, a hierarchy is introduced into the hypergraph model for modeling hierarchical genomic relationships. This hierarchical structure allows the hypergraph model to explicitly represent gene regulatory circuits as functional blocks or groups across the level of epigenetic, transcriptional, and post-transcriptional regulation. Moreover, the proposed graph-analyzing method is able to grasp the global structures of biological systems such as genomic modules and regulatory networks by analyzing the learned model structures. The proposed model is applied to analyzing cancer genomics considered as a major topic in current biology and medicine. We show that the performance of our model competes with or outperforms state-of-the-art models on multiple cancer genomic data. Furthermore, the propose model is capable of discovering new or hidden patterns as candidates of potential gene regulatory circuits such as gene modules, miRNA-mRNA networks, and multiple genomic interactions, associated with the specific cancer. The results of these analysis can provide several crucial evidences that can pave the way for identifying unknown functions in the cancer system. The proposed hypergraph model will contribute to elucidating core regulatory mechanisms and to comprehensive understanding of biological processes including cancers.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .i 1 Introduction 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problems to be Addressed . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 The Proposed Approach and its Contribution . . . . . . . . . . . . . . 4 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . 6 2 Related Work 2.1 Analysis of Co-Regulatory Genomic Interactions from Omics Data . . 9 2.2 Probabilistic Graphical Models for Biological Problems . . . . . . . . 11 2.2.1 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Higher-order Graphical Models for Biological Problems . . . . . . . . 16 2.3.1 Higher-Order Models . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.2 Hypergraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Hypergraph Classifiers for Identifying Prognostic Modules in Cancer 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Analyzing Gene Modules for Cancer Prognosis Prediction . . . . . . 24 3.3 Hypergraph Classifiers for Identifying Cancer Gene Modules . . . . 26 3.3.1 Hypergraph Classifiers . . . . . . . . . . . . . . . . . . . . . . 26 3.3.2 Bayesian Evolutionary Algorithm . . . . . . . . . . . . . . . . 27 3.3.3 Bayesian Evolutionary Learning for Hypergraph Classifiers . 29 3.4 Predicting Cancer Clinical Outcomes Based on Gene Modules . . . . 34 3.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 34 3.4.2 Prediction Performance . . . . . . . . . . . . . . . . . . . . . . 36 3.4.3 Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4.4 Identification of Prognostic Gene Modules . . . . . . . . . . . 44 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4 Hypergraph-based Models for Constructing Higher-Order miRNA-mRNA Interaction Networks in Cancer 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.2 Analyzing Relationships between miRNAs and mRNAs from Heterogeneous Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3 Hypergraph-based Models for Identifying miRNA-mRNA Interactions 57 4.3.1 Hypergraph-based Models . . . . . . . . . . . . . . . . . . . . 57 4.3.2 Learning Hypergraph-based Models . . . . . . . . . . . . . . . 61 4.3.3 Building Interaction Networks from Hypergraphs . . . . . . . 64 4.4 Constructing miRNA-mRNA Interaction Networks Based on Higher- Order Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 66 4.4.2 Classification Performance . . . . . . . . . . . . . . . . . . . . 68 4.4.3 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 70 CONTENTS iii 4.4.4 Constructed Higher-Order miRNA-mRNA Interaction Networks in Prostate Cancer . . . . . . . . . . . . . . . . . . . . . 74 4.4.5 Functional Analysis of the Constructed Interaction Networks 78 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Hierarchical Hypergraphs for Identifying Higher-Order Genomic Interactions in Multilevel Regulation 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2 Analyzing Epigenetic and Genetic Interactions from Multiple Genomic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3 Hierarchical Hypergraphs for Identifying Epigenetic and Genetic Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3.1 Hierarchical Hypergraphs . . . . . . . . . . . . . . . . . . . . . 92 5.3.2 Learning Hierarchical Hypergraphs . . . . . . . . . . . . . . . 95 5.4 Identifying Higher-Order Genomic Interactions in Multilevel Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.4.1 Data and Experimental Settings . . . . . . . . . . . . . . . . . 100 5.4.2 Identified Higher-Order miRNA-mRNA Interactions Induced by DNA Methylation in Ovarian Cancer . . . . . . . . . . . . 102 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Concluding Remarks 6.1 Summary of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . 107 6.2 Directions for Further Research . . . . . . . . . . . . . . . . . . . . . . 109 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 초록 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Docto

Statistical Methods for Integrating Genomics Data

This dissertation focuses on methodology to integrate multiplatform genomic data with cancer applications. Such integration facilitates the discovery of biological information crucial to the development of targeted treatments. We present iBAG (integrative Bayesian Analysis of Genomics data), a two-step hierarchical Bayesian model that uses the known biological relationships between genetic platforms to integrate an arbitrary number of platforms in a single model. This method identifies genes important to a clinical outcome, such as survival, and the integration approach also allows us to identify which platforms are modulating the important gene effects. A glioblastoma multiforme (GBM) data set publicly available from The Cancer Genome Atlas (TCGA) is analyzed with iBAG. We flag several genes as important to survival time, and we include a discussion of these genes in a biological context. We then present a nonlinear formulation of iBAG, which increases the flexibility of the model to accommodate nonlinear relationships among the data platforms. The TCGA GBM data is again analyzed, and we carefully compare the results from both the linear and nonlinear formulation. Next we present a pathway iBAG model, piBAG, which includes gene pathway membership information and utilizes hierarchical shrinkage to simultaneously select important genes and assign pathway scores. The integration of multiple genomic platforms again allows us to determine which platform is regulating each important gene, and it also provides insight as to through which platform each pathway is taking effect. We apply this method to a different subset of the TCGA GBM data. Finally, we present integrative heatmaps, a novel visualization tool for illustrating integrated data. We use a TCGA colorectal cancer data set to demonstrate the integrative heatmaps. Through the various simulation studies and data applications in this dissertation, we conclude that the methods presented achieve their respective goals and outperform standard methods. We demonstrate that our methods provide many advantages, including increased estimation efficiency, increased power, lower false discovery rates, and deeper biological insight into the genetic mechanics of cancer development and progression

INTEGRATION OF MULTI-PLATFORM HIGH-DIMENSIONAL OMIC DATA

The development of high-throughput biotechnologies have made data accessible from different platforms, including RNA sequencing, copy number variation, DNA methylation, protein lysate arrays, etc. The high-dimensional omic data derived from different technological platforms have been extensively used to facilitate comprehensive understanding of disease mechanisms and to determine personalized health treatments. Although vital to the progress of clinical research, the high dimensional multi-platform data impose new challenges for data analysis. Numerous studies have been proposed to integrate multi-platform omic data; however, few have efficiently and simultaneously addressed the problems that arise from high dimensionality and complex correlations. In my dissertation, I propose a statistical framework of shared informative factor model (SIFORM) that can jointly analyze multi-platform omic data and explore their associations with a disease phenotype. The common disease- associated sample characteristics across different data types can be captured through the shared structure space, while the corresponding weights of genetic variables directly index the strengths of their association with the phenotype. I compare the performance of the proposed method with several popular regularized regression methods and canonical correlation analysis (CCA)-based methods through extensive simulation studies and two lung adenocarcinoma applications. The two lung adenocarcinoma applications jointly explore the associations of mRNA expression and protein expression with smoking status and survival using The Cancer Genome Atlas (TCGA) datasets. The simulation studies demonstrate the superior performance of SIFORM in terms of biomarker detection accuracy. In lung cancer applications, SIFORM identifies many biomarkers that belong to key pathways for lung tumorigenesis. It also discovers potential prognostic biomarkers for lung cancer patients survival and some biomarkers that reveal different tumorigenesis mechanisms between light smokers and heavy smokers. To improve the prediction accuracy and interpretability of the proposed model, I extend it to PSIFORM by incorporating existing biological pathway information to current statistical framework. I adopt a network-based regularization to ensure that the neighboring genes in the same pathway tend to be selected (or eliminated) simultaneously. Through simulation studies and a TCGA kidney cancer application, I show that PSIFORM outperforms its competitors in both variable selection and prediction. The statistical framework of PSIFORM also has a great potential in incorporating the hierarchical order across the multi-platform omic measurements