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Machine Learning For The Genotype To Phenotype Problem
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Book Synopsis Machine Learning for the Genotype-to-Phenotype Problem by : John William Santerre
Download or read book Machine Learning for the Genotype-to-Phenotype Problem written by John William Santerre and published by . This book was released on 2018 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis demonstrates the suitability of machine learning for classifying phenotypes from genotype data. First, we analyze the suitability of machine learning techniques on antimicrobial resistance phenotypes. Additionally, we evaluate the stability of identifying DNA regions related to antimicrobial resistance. To speed and simplify this process, we develop a unique matrix construction method specifically for use on antimicrobial resistances datasets. We also consider an alternative phenotypic classification problem — namely predicting the ability of an organism to grow on a particular media (predicting growth rate) and the structure of the resulting feature space. Finally, we propose an extension of the Random Forest feature importance calculation and show how such an alteration results in an improvement in the identification of gene regions.
Book Synopsis Machine Learning for Microbial Phenotype Prediction by : Roman Feldbauer
Download or read book Machine Learning for Microbial Phenotype Prediction written by Roman Feldbauer and published by Springer. This book was released on 2016-06-15 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data.
Book Synopsis Novel Machine Learning Models for Identification, Characterization and Prioritization of Phenotype-Genotype Associations by : Recep Colak
Download or read book Novel Machine Learning Models for Identification, Characterization and Prioritization of Phenotype-Genotype Associations written by Recep Colak and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Inferring Phenotypes from Genotypes with Machine Learning by : Alexandre Drouin
Download or read book Inferring Phenotypes from Genotypes with Machine Learning written by Alexandre Drouin and published by . This book was released on 2019 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough understanding of the relationship between the genomic characteristics of an individual (the genotype) and its biological state (the phenotype) is essential to personalized medicine, where treatments are tailored to each individual. This notably allows to anticipate diseases, estimate response to treatments, and even identify new pharmaceutical targets. Machine learning is a science that aims to develop algorithms that learn from examples. Such algorithms can be used to learn models that estimate phenotypes based on genotypes, which can then be studied to elucidate the biological mechanisms that underlie the phenotypes. Nonetheless, the application of machine learning in this context poses significant algorithmic and theoretical challenges. The high dimensionality of genomic data and the small size of data samples can lead to overfitting; the large volume of genomic data requires adapted algorithms that limit their use of computational resources; and importantly, the learned models must be interpretable by domain experts, which is not always possible. This thesis presents learning algorithms that produce interpretable models for the prediction of phenotypes based on genotypes. Firstly, we explore the prediction of discrete phenotypes using rule-based learning algorithms. We propose new implementations that are highly optimized and generalization guarantees that are adapted to genomic data. Secondly, we study a more theoretical problem, namely interval regression. We propose two new learning algorithms, one which is rule-based. Finally, we show that this type of regression can be used to predict continuous phenotypes and that this leads to models that are more accurate than those of conventional approaches in the presence of censored or noisy data. The overarching theme of this thesis is an application to the prediction of antibiotic resistance, a global public health problem of high significance. We demonstrate that our algorithms can be used to accurately predict resistance phenotypes and contribute to the improvement of their understanding. Ultimately, we expect that our algorithms will take part in the development of tools that will allow a better use of antibiotics and improved epidemiological surveillance, a key component of the solution to this problem.
Book Synopsis Investigation of Methods for Machine Learning Associations Between Genetic Varations and Phenotype by : Amanda M. Hartung
Download or read book Investigation of Methods for Machine Learning Associations Between Genetic Varations and Phenotype written by Amanda M. Hartung and published by . This book was released on 2016 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The relationship between genetics and phenotype is a complex one that remains poorly understood. Many factors contribute to the relationship between genetic variations and differences in phenotype. An improved understanding of the genetic underpinnings of various phenotypes can help us make important advances in testing for, preventing, treating, and curing a number of diseases and disorders. The recent popularization of direct-to-consumer sequencing services, coupled with consumers releasing their genetic information for public use, has led to an unprecedented level of access to genetic information. Crowd-sourcing the problem of developing robust genome-wide association techniques for ever larger amounts of data is a promising trend. This thesis explores likely methods to data mine one such public genetic data repository, openSNP, for correlated genotypes and phenotypes. Particular care is given to data clean-up and the steps required to preprocess public data for machine learning. The preprocessing methods are detailed in such a way that they may be applied to other genetic data repositories that already exist, for example the Personal Genome Project, as well as genetic data repositories that may become available in the future. Following data clean-up, a number of machine learning techniques are investigated, applied, and assessed for their utility in such a big-data problem. No single machine learning approach was found to be sufficient; the combination of imbalanced phenotype response classes and an underdetermined system led to a difficult machine learning challenge. Additional techniques must be explored or developed in order to make such genome-wide association studies possible and meaningful."--Abstract.
Book Synopsis Elements of Causal Inference by : Jonas Peters
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Book Synopsis Artificial Intelligence by : Marco Antonio Aceves-Fernandez
Download or read book Artificial Intelligence written by Marco Antonio Aceves-Fernandez and published by BoD – Books on Demand. This book was released on 2018-06-27 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Walter Daelemans
Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 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.
Book Synopsis 2019 IEEE First International Conference on Cognitive Machine Intelligence by :
Download or read book 2019 IEEE First International Conference on Cognitive Machine Intelligence written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Statistical Relational Learning by : Lise Getoor
Download or read book Introduction to Statistical Relational Learning written by Lise Getoor and published by MIT Press. This book was released on 2019-09-22 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
Book Synopsis Phenotypic Plasticity by : Thomas J. DeWitt
Download or read book Phenotypic Plasticity written by Thomas J. DeWitt and published by Oxford University Press, USA. This book was released on 2004 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic, evolution, adaptation, environment, genotype.
Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Book Synopsis Systems and Synthetic Biology by : Vikram Singh
Download or read book Systems and Synthetic Biology written by Vikram Singh and published by Springer. This book was released on 2014-12-15 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook has been conceptualized to provide a detailed description of the various aspects of Systems and Synthetic Biology, keeping the requirements of M.Sc. and Ph.D. students in mind. Also, it is hoped that this book will mentor young scientists who are willing to contribute to this area but do not know from where to begin. The book has been divided into two sections. The first section will deal with systems biology – in terms of the foundational understanding, highlighting issues in biological complexity, methods of analysis and various aspects of modelling. The second section deals with the engineering concepts, design strategies of the biological systems ranging from simple DNA/RNA fragments, switches and oscillators, molecular pathways to a complete synthetic cell will be described. Finally, the book will offer expert opinions in legal, safety, security and social issues to present a well-balanced information both for students and scientists.
Book Synopsis Neural Networks in Finance and Investing by : Robert R. Trippi
Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1996 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine by : Tao Zeng
Download or read book Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine written by Tao Zeng and published by Frontiers Media SA. This book was released on 2020-03-30 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Oil Palm Breeding written by Aik Chin Soh and published by CRC Press. This book was released on 2017-08-14 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: The oil palm is a remarkable crop, producing around 40% of the world’s vegetable oil from around 6% of the land devoted to oil crops. Conventional breeding has clearly been the major focus of genetic improvement in this crop. A mix of improved agronomy and management, coupled with breeding selection have quadrupled the oil yield of the crop since breeding began in earnest in the 1920s. However, as for all perennial crops with long breeding cycles, oil palm faces immense challenges in the coming years with increased pressure from population growth, climate change and the need to develop environmentally sustainable oil palm plantations. In Oil Palm: Breeding, Genetics and Genomics, world leading organizations and individuals who have been at the forefront of developments in this crop, provide their insights and experiences of oil palm research, while examining the different challenges that face the future of the oil palm. The editors have all been involved in research and breeding of oil palm for many years and use their knowledge of the crop and their disciplinary expertise to provide context and to introduce the different research topics covered.
Book Synopsis Genetic Algorithm Essentials by : Oliver Kramer
Download or read book Genetic Algorithm Essentials written by Oliver Kramer and published by Springer. This book was released on 2017-01-07 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
