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

Compositional Mining of Multi-Relational Biological Datasets

High-throughput biological screens are yielding ever-growing streams of information about multiple aspects of cellular activity. As more and more categories of datasets come online, there is a corresponding multitude of ways in which inferences can be chained across them, motivating the need for compositional data mining algorithms. In this paper, we argue that such compositional data mining can be effectively realized by functionally cascading redescription mining and biclustering algorithms as primitives. Both these primitives mirror shifts of vocabulary that can be composed in arbitrary ways to create rich chains of inferences. Given a relational database and its schema, we show how the schema can be automatically compiled into a compositional data mining program, and how different domains in the schema can be related through logical sequences of biclustering and redescription invocations. This feature allows us to rapidly prototype new data mining applications, yielding greater understanding of scientific datasets. We describe two applications of compositional data mining: (i) matching terms across categories of the Gene Ontology and (ii) understanding the molecular mechanisms underlying stress response in human cells

Deep Learning for Embedding and Integrating Multimodal Biomedical Data

Biomedical data is being generated in extremely high throughput and high dimension by technologies in areas ranging from single-cell genomics, proteomics, and transcriptomics (cytometry, single-cell RNA and ATAC sequencing) to neuroscience and cognition (fMRI and PET) to pharmaceuticals (drug perturbations and interactions). These new and emerging technologies and the datasets they create give an unprecedented view into the workings of their respective biological entities. However, there is a large gap between the information contained in these datasets and the insights that current machine learning methods can extract from them. This is especially the case when multiple technologies can measure the same underlying biological entity or system. By separately analyzing the same system but from different views gathered by different data modalities, patterns are left unobserved if they only emerge from the multi-dimensional joint representation of all of the modalities together. Through an interdisciplinary approach that emphasizes active collaboration with data domain experts, my research has developed models for data integration, extracting important insights through the joint analysis of varied data sources. In this thesis, I discuss models that address this task of multi-modal data integration, especially generative adversarial networks (GANs) and autoencoders (AEs). My research has been focused on using both of these models in a generative way for concrete problems in cutting-edge scientific applications rather than the exclusive focus on the generation of high-resolution natural images. The research in this thesis is united around ideas of building models that can extract new knowledge from scientific data inaccessible to currently existing methods

Personalized Medicine: Studies of Pharmacogenomics in Yeast and Cancer

Advances in microarray and sequencing technology enable the era of personalized medicine. With increasing availability of genomic assays, clinicians have started to utilize genetics and gene expression of patients to guide clinical care. Signatures of gene expression and genetic variation in genes have been associated with disease risks and response to clinical treatment. It is therefore not difficult to envision a future where each patient will have clinical care that is optimized based on his or her genetic background and genomic profiles. However, many challenges exist towards the full realization of the potential personalized medicine. The human genome is complex and we have yet to gain a better understanding of how to associate genomic data with phenotype. First, the human genome is very complex: more than 50 million sequence variants and more than 20,000 genes have been reported. Many efforts have been devoted to genome-wide association studies (GWAS) in the last decade, associating common genetic variants with common complex traits and diseases. While many associations have been identified by genome-wide association studies, most of our phenotypic variation remains unexplained, both at the level of the variants involved and the underlying mechanism. Finally, interaction between genetics and environment presents additional layer of complexity governing phenotypic variation. Currently, there is much research developing computational methods to help associate genomic features with phenotypic variation. Modeling techniques such as machine learning have been very useful in uncovering the intricate relationships between genomics and phenotype. Despite some early successes, the performance of most models is disappointing. Many models lack robustness and predictions do not replicate. In addition, many successful models work as a black box, giving good predictions of phenotypic variation but unable to reveal the underlying mechanism. In this thesis I propose two methods addressing this challenge. First, I describe an algorithm that focuses on identifying causal genomic features of phenotype. My approach assumes genomic features predictive of phenotype are more likely to be causal. The algorithm builds models that not only accurately predict the traits, but also uncover molecular mechanisms that are responsible for these traits. . The algorithm gains its power by combining regularized linear regression, causality testing and Bayesian statistics. I demonstrate the application of the algorithm on a yeast dataset, where genotype and gene expression are used to predict drug sensitivity and elucidate the underlying mechanisms. The accuracy and robustness of the algorithm are both evaluated statistically and experimentally validated. The second part of the thesis takes on a much more complicated system: cancer. The availability of genomic and drug sensitivity data of cancer cell lines has recently been made available. The challenge here is not only the increasing complexity of the system (e.g. size of genome), but also the fundamental differences between cancers and tissues. Different cancers or tissues provide different contexts influencing regulatory networks and signaling pathways. In order to account for this, I propose a method to associate contextual genomic features with drug sensitivity. The algorithm is based on information theory, Bayesian statistics, and transfer learning. The algorithm demonstrates the importance of context specificity in predictive modeling of cancer pharmacogenomics. The two complementary algorithms highlight the challenges faced in personalized medicine and the potential solutions. This thesis detailed the results and analysis that demonstrate the importance of causality and context specificity in predictive modeling of drug response, which will be crucial for us towards bringing personalized medicine in practice

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

Method and System for Identification of Metabolites Using Mass Spectra

A method and system is provided for mass spectrometry for identification of a specific elemental formula for an unknown compound which includes but is not limited to a metabolite. The method includes calculating a natural abundance probability (NAP) of a given isotopologue for isotopes of non-labelling elements of an unknown compound. Molecular fragments for a subset of isotopes identified using the NAP are created and sorted into a requisite cache data structure to be subsequently searched. Peaks from raw spectrum data from mass spectrometry for an unknown compound. Sample-specific peaks of the unknown com- pound from various spectral artifacts in ultra-high resolution Fourier transform mass spectra are separated. A set of possible isotope-resolved molecular formula (IMF) are created by iteratively searching the molecular fragment caches and combining with additional isotopes and then statistically filtering the results based on NAP and mass-to-charge (m/2) matching probabilities. An unknown compound is identified and its corresponding elemental molecular formula (EMF) from statistically-significant caches of isotopologues with compatible IMFs

Improving Risk Factor Identification of Human Complex Traits in Omics Data

With recent advances in various high throughput technologies, the rise of omics data offers a promise of personalized health care with its potential to expand both the depth and the width of the identification of risk factors that are associated with human complex traits. In genomics, the introduction of repeated measures and the increased sequencing depth provides an opportunity for deeper investigation of disease dynamics for patients. In transcriptomics, high throughput single-cell assays provide cellular level gene expression depicting cell-to-cell heterogeneity. The cell-level resolution of gene expression data brought the opportunities to promote our understanding of cell function, disease pathogenesis, and treatment response for more precise therapeutic development. Along with these advances are the challenges posed by the increasingly complicated data sets. In genomics, as repeated measures of phenotypes are crucial for understanding the onset of disease and its temporal pattern, longitudinal designs of omics data and phenotypes are being increasingly introduced. However, current statistical tests for longitudinal outcomes, especially for binary outcomes, depend heavily on the correct specification of the phenotype model. As many diseases are rare, efficient designs are commonly applied in epidemiological studies to recruit more cases. Despite the enhanced efficiency in the study sample, this non-random ascertainment sampling can be a major source of model misspecification that may lead to inflated type I error and/or power loss in the association analysis. In transcriptomics, the analysis of single-cell RNA-seq data is facing its particular challenges due to low library size, high noise level, and prevalent dropout events. The purpose of this dissertation is to provide the methodological foundation to tackle the aforementioned challenges. We first propose a set of retrospective association tests for the identification of genetic loci associated with longitudinal binary traits. These tests are robust to different types of phenotype model misspecification and ascertainment sampling design which is common in longitudinal cohorts. We then extend these retrospective tests to variant-set tests for genetic rare variants that have low detection power by incorporating the variance component test and burden test into the retrospective test framework. Finally, we present a novel gene-graph based imputation method to impute dropout events in single-cell transcriptomic data to recover true gene expression level by borrowing information from adjacent genes in the gene graph