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

Graph Representation Learning in Biomedicine

Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge. With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing, and learning with such networks. In this review, we put forward an observation that long-standing principles of networks in biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces, and capture the breadth of ways in which representation learning is proving useful. Areas of profound impact include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates

A Long-Term Neuroepigenomic Profile of Prenatal Alcohol Exposure

Fetal Alcohol Spectrum Disorders (FASD) represent the largest preventable cause of cognitive deficits in the western world. The mechanism(s) of how prenatal alcohol exposure (PAE) results in FASD remain unknown. Towards this end, mouse models of PAE have successfully recreated endophenotypes that are characteristic of FASD. This doctoral thesis examines the long-term epigenomic alterations associated with PAE. I have examined both mice with PAE and human patients with FASD. In the first set of experiments, mice with PAE and matched controls were raised to adulthood and then their whole brains were examined for alterations to gene expression, non-coding RNA (ncRNA) expression, and DNA methylation. Long-term alterations were observed in genes related to neurodevelopment, cellular signaling, and immune processes. Furthermore, there was an enrichment for alterations to genomically imprinted clusters of ncRNA and genes related to PTEN/PI3K/AKT/mTOR signaling. In the second set of experiments, buccal epithelial swabs were collected from young children with FASD and matched controls. Children from a discovery cohort were examined for alterations to DNA methylation, which revealed changes to genes involved in neurodevelopment and synaptic signaling as well as hippo signaling. Select candidates (COLEC11 and HTT) were confirmed by sodium bisulfite pyrosequencing. Examination of a replication cohort revealed that while similar pathways are altered, the effect is not identical and that sex and age may alter the methylation profile. A larger group of children, representative of the general population, were then analyzed using a targeted sodium bisulfite next-generation sequencing panel and pyrosequencing. No single gene examined was found to be consistently affected in all FASD children. Finally, the mouse and human results were compared to identify alterations to shared loci, ontologies, and pathways. The clustered protocadherins, which are involved in generating individual neuronal identity, showed increased DNA methylation in both species. Together, the results suggest that a shared DNA methylation profile related to neurodevelopment is present in both the brains of adult mice and the buccal epithelial swabs of young children with PAE. These results may be used in future functional studies of candidate loci as well as towards the development of much needed diagnostics and precision medicine

Functional genomics studies of human brain development and implications for autism spectrum disorder

Human neurodevelopment requires the coordinated expression of thousands of genes, exquisitely regulated in both spatial and temporal dimensions, to achieve the proper specialization and inter-connectivity of brain regions. Consequently, the dysregulation of complex gene networks in the developing brain is believed to underlie many neurodevelopmental disorders, such as autism spectrum disorders (ASD). Autism has a significant genetic etiology, but there are hundreds of genes implicated, and their functions are heterogeneous and complex. Therefore, an understanding of shared molecular and cellular pathways underlying the development ASD has remained elusive, hampering attempts to develop common diagnostic biomarkers or treatments for this disorder. I hypothesized that analyzing functional genomics relationships among ASD candidate genes during normal human brain development would provide insight into common cellular and molecular pathways that are affected in autistic individuals, and may help elucidate how hundreds of diverse genes can all be linked to a single clinical phenotype. This thesis describes a coordinated set of bioinformatics experiments that first (i) assessed for gene expression and co-expression properties among ASD candidates and other non-coding RNAs during normal human brain development to discover potential shared mechanisms; and then (ii) directly assessed for changes in these pathways in autistic post-mortem brain tissue. The results demonstrated that when examined in the context of normal human brain gene expression during early development, autism candidate genes appear to be strongly related to the neurodevelopmental pathways of synaptogenesis, mitochondrial function, glial cytokine signaling, and transcription/translation regulation. Furthermore, the known sex bias in ASD prevalence appeared to relate to differences in gene expression between the developing brains of males and females. Follow up studies in autistic brain tissue confirmed that changes in mitochondrial gene expression networks, glial pathways, and gene expression regulatory mechanisms are all altered in the brains of autistic individuals. Together, these results show that the heterogeneous set of autism candidate genes are related to each other through shared transcriptional networks that funnel into common molecular mechanisms, and that these mechanisms are aberrant in autistic brains

RNA Interference

RNA interference (RNAi), a hallmark of all biological sciences of twenty-first century, is an evolutionarily conserved and double-stranded RNA-dependent eukaryotic cell defense process. Opportunity to utilize an organisms own gene and to systematically induce and trigger RNAi for any desired sequence made RNAi an efficient approach for functional genomics, providing a solution for conventional longstanding obstacles in life sciences. RNAi research and application have significantly advanced during past two decades. This book RNA interference provides an updated knowledge and progress on RNAi in various organisms, explaining basic principles, types, and property of inducers, structural modifications, delivery systems/methodologies, and various successful bench-to-field or clinic applications and disease therapies with some aspects of limitations, alternative tools, safety, and risk assessment