Machine Learning For The Study Of Gene Regulation And Complex Traits

With the development of next-generation sequencing technologies, we can detect numerous genetic variants associated with many diseases or complex traits over the past decades. Genome-wide association studies (GWAS) have been one of the most effective methods to identify those variants. It discovers disease-associated variants by comparing the genetic information between controls and cases. This approach is simple and effective and has been used by many studies. Before performing GWAS, we need to detect the genetic variants of the sample population. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. Here, I will present ForestQC, an efficient statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach, which outperforms widely used methods by considerably improving the quality of variants to be included in the analysis. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases, especially whether or how they regulate gene expression as they may affect diseases through gene regulation. However, it is challenging to identify the regulatory effects of rare variants because it often requires large sample sizes and the existing statistical approaches are not optimized for it. To improve statistical power, I will introduce a new approach, LRT-q, based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. I apply LRT-q to the GTEx dataset and find many novel biological insights. Recent studies have shown that omics data can be used for automatic disease diagnosis with machine learning algorithms. I will introduce an accurate and automated machine learning pipeline for the diagnosis of atopic dermatitis (AD) based on transcriptome and microbiota data. I will demonstrate that this classifier can accurately differentiate subjects with AD and healthy individuals. It also identifies a set of genes and microorganisms that are predictive for AD. I will show that they are directly or indirectly associated with AD.

Complex systems are ubiquitous in genetics and genomics. From the regulation of gene expression to the genetic basis of complex traits, we see that complex networks of diverse cellular molecules underpin the natural world. Driven by technological advances, today's researchers have access to large amounts of omics data from diverse species. At the same time, improvements in computer processing and algorithms have produced more powerful computational tools. Taken together, these advances mean that those working at the interface of data science and biology are poised to better model and understand complex biological systems. The research in this dissertation demonstrates how a data-driven approach can be used to better understand three complex systems: (1) transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana, (2) the genetic basis of flowering time, a complex trait, in Zea mays, and (3) the social basis for opinions and beliefs about biotechnology products.To study the first system, we generated models of the cis-regulatory code from information about DNA sequence and additional omics levels using both classic machine learning and deep learning algorithms. We identified 1,061 putative cis-regulatory elements associated with different patterns of response to single and combined heat and drought stress and found that information about additional levels of regulation, especially chromatin accessibility and known transcription factor binding, improved our models of the cis-regulatory code. To study the second system, we generated phenotype prediction models for flowering time, height, and yield based on either genetic markers or transcript levels at the seedling stage. We found that, while genetic marker-based models performed better than transcript level-based models, models that integrated both types of data performed best. Furthermore, transcript-based models were more useful for finding genes known to be associated with flowering time, highlighting how using additional levels of omics data can improve our ability to understand the genetic basis of complex traits. Finally, to study the third system, we integrated 29 characteristics about a person (e.g. age, political ideology, education, values, environmental beliefs) into a machine learning model that would predict an individual's beliefs and opinions about five different types of biotechnology products (e.g. biofortification, biopharmaceuticals). While this approach was particularly usefully for identifying individuals that were broadly supportive of biotechnology, finding characteristics of individuals with negative or conditional (i.e. support product A, but not B) opinions was more challenging, highlighting the complexity of public opinions about biotechnology.

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Abstract : The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases. This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, poplar, and maize. Challenges arise due to limited training data for gene regulatory pairs, especially in less-studied species such as poplar and maize. Nonetheless, our results demonstrate that hybrid models integrating ML and artificial neural network (ANN) techniques significantly outperformed traditional methods in predicting gene regulatory relationships. The best-performing hybrid models achieved over 95% accuracy on holdout test datasets, surpassing traditional ML and ANN models and also showed good accuracy on lignin biosynthesis pathway analysis. Employing transfer learning techniques, this study has also successfully transferred the known knowledge of gene regulation from one species to another, substantially improving performance and manifesting the viability of cross-species learning using deep learning-based approaches. This study contributes to the methodology for growing body of knowledge in GRN prediction and construction for plant species, highlighting the value of adopting hybrid models and transfer learning techniques. This study and the results will help to pave a way for future research on how to learn from known to unknown and will be conductive to the advance of modern genomics and bioinformatics.

This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

The field of genetics is rapidly evolving, and new medical breakthroughs are occurring as a result of advances in knowledge gained from genetics reasearch. This thematic volume of Advances in Genetics looks at Computational Methods for Genetics of Complex traits. Explores the latest topics in neural circuits and behavior research in zebrafish, drosophila, C.elegans, and mouse models Includes methods for testing with ethical, legal, and social implications Critically analyzes future prospects