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Inferring Phenotypes From Genotypes With Machine Learning
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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 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 Statistical Methods for Inferring Correlation and Causation Between Genotypes and Phenotypes by : Nathan Riley Summers LaPierre
Download or read book Statistical Methods for Inferring Correlation and Causation Between Genotypes and Phenotypes written by Nathan Riley Summers LaPierre and published by . This book was released on 2022 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-Wide Association Studies (GWAS) have identified many genetic variants that are associated with a variety of complex phenotypes, including anthropometric and lifestyle traits as well as complex diseases. It is unclear, however, which of these variants actually play causal roles in these phenotypes, as opposed to simply being correlated with the causal variants. It is also unclear through which intermediate mechanisms causal variants impact complex phenotypes, such as effects on gene expression, metabolites, the microbiome, or other related phenotypes. In this dissertation, I present computational and statistical methods for addressing these issues. These methods infer causal variants for complex phenotypes, link variants to intermediate gene expression phenotypes, and use genetic variants to determine the causal effect of intermediate phenotypes on downstream phenotypes. Further exploring one such set of intermediate phenotypes, I present several methods for the analysis of metagenomic sequencing data. Metagenomics, the study of microbial genomes sequenced directly from their host environment, has revolutionized the study of microorganisms and illuminated their key roles in environmental function and dysfunction, including in human health and disease. However, it is challenging to determine which microbes are present and their relative abundances from sequencing data, due to incomplete genomic reference databases as well as errors in the sequencing reads themselves. I introduce several methods addressing this challenge, providing means to correct errors in sequencing reads and then to estimate the relative abundances of microbial taxa in the sequenced sample. I then explore several machine learning approaches for predicting human diseases based on inferred microbe abundance information.
Book Synopsis Machine Learning for the Study of Gene Regulation and Complex Traits by : Anne Sonnenschein
Download or read book Machine Learning for the Study of Gene Regulation and Complex Traits written by Anne Sonnenschein and published by . This book was released on 2017 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Sparse Model Learning for Inferring Genotype and Phenotype Associations by : Anhui Huang
Download or read book Sparse Model Learning for Inferring Genotype and Phenotype Associations written by Anhui Huang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genotype and phenotype associations are of paramount importance in understanding the genetic basis of living organisms, improving traits of interests in animal and plant breeding, as well as gaining insights into complex biological systems and the etiology of human diseases. With the advancements in molecular biology such as microarrays, high throughput next generation sequencing, RNAseq, et al, the number of available genotype markers is far exceeding the number of available samples in association studies. The objective of this dissertation is to develop sparse models for such high dimensional data, develop accurate sparse variable selection and estimation algorithms for the models, and design statistical methods for robust hypothesis tests for the genotype and phenotype associations. We develop a novel empirical Bayesian least absolute shrinkage and selection operator (EBlasso) algorithm with Normal, Exponential and Gamma (NEG), and Normal, Exponential (NE) hierarchical prior distributions, and an empirical Bayesian elastic net (EBEN) algorithm with an innovative Normal and generalized Gamma (NG) hierarchical prior distribution, for both general linear and generalized logistic regression models. Both of the two empirical Bayes methods estimate variance components of the regression coefficients with closed-form solutions and perform automatic variable selection such that a variable with zero variance is excluded from the model. With the closed-form solutions for variance components in the model and without estimating the posterior modes for excluded variables, the two empirical Bayes methods infer sparse models efficiently. Having both covariance and posterior modes estimated, they also provide a statistical testing method that considers as much information as possible without increasing the degrees of freedom (DF). Extensive simulation studies are carried out to evaluate the performance of the proposed methods, and real datasets are analyzed for validation. Both simulation and real data analyses suggest that the two methods are fast and accurate genotype-phenotype association methods that can easily handle high dimensional data including possible main and interaction effects. Comparing the two methods, EBlasso typically selects one variable out of a group of highly correlated effects, and the EBEN algorithm encourages a grouping effect that selects a group of effects if they are correlated. Not only verificatory simulation and real dataset analyses are performed, we further demonstrate the advantage of the developed algorithms through two exploratory applications, namely the whole-genome QTL mapping for an elite rice hybrid and pathway-based genome wide association study (GWAS) for human Parkinson disease (PD). In the first application, we exploit whole-genome markers of an immortalized F2 population derived from an elite rice hybrid to perform QTL mapping for the rice-yield phenotype. Our QTL model includes additive and dominance main effects of 1,619 markers and all pair-wise interactions, with a total of more than 5 million possible effects. This study not only reveals the major role of epistasis influencing rice yield, but also provides a set of candidate genetic loci for further experimental investigations. In the second application, we employ the EBlasso logistic regression model for pathway-based GWAS to include all possible main effects and a large number of pair-wise interactions of single nucleotide polymorphisms (SNPs) in a pathway, with a total number of more than 32 million effects included in the model. With effects inferred by EBlasso, the statistical significance of a pathway is tested with the Wald statistics and reliable effects in a significant pathway are selected using the stability selection technique. Another important area of genotype and phenotype association is to infer the structure of gene regulatory networks (GRNs). We developed a GRN inference algorithm by exploring sparse model selection and estimation methods in structural equation models (SEMs). We extend a previously developed sparse-aware maximum likelihood (SML) algorithm to incorporate the adaptive elastic net penalty for the SEM likelihood function (SEM-EN) and infer the model using a parallelized block coordinate ascent algorithm. With the versatile penalty function and powerful parallel computation, the SEM-EN algorithm is able to infer a network with thousands of nodes. The performance of the developed algorithm are demonstrated through simulation studies, in which power of detection and false discovery rate both suggest that SEM-EN significantly improves GRN inference over the previously developed SEM-SML algorithm. When applied to infer the GRN of a real budding yeast dataset with more than 3,000 nodes, SEM-EN infers a sparse network corroborated by previous independent studies in terms of roles of hub nodes and functions of key clusters. Given the fundamental importance of genotype and phenotype associations in understanding the genetic basis of complex biological system, the EBlasso-NE, EBlasso-NEG, EBEN, as well as SEM-EN algorithms and software packages developed in this dissertation achieve the effectiveness, robustness and efficiency that are needed for successful application to biology. With the advancement of high-throughput molecular technologies in generating information at genetic, epigenetic, transcriptional and post-transcriptional levels, the methods developed in this dissertation can have broad applications to infer different types of genotype and phenotypes associations.
Book Synopsis Learning Genomic and Molecular Mediators of Genotype-phenotype Associations by : Anna Shcherbina
Download or read book Learning Genomic and Molecular Mediators of Genotype-phenotype Associations written by Anna Shcherbina and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The vast majority of genomic variants are non-coding, and many disrupt regulatory elements, causing dysregulation of gene expression. However, the functional mechanisms by which non-coding variants operate at the molecular level, as well as their tissue-specific downstream effects on cellular, organismal and disease phenotypes remain challenging to decipher. Firstly, complex phenotypes such as physical activity patterns are difficult to characterize and measure. Secondly, even after inferring statistical associations between genetic loci and complex phenotypes, identifying the causal variants is challenging due to the issues posed by linkage disequilibrium. Finally, the elucidation of functional molecular mechanisms that mediate the manifestation of genotypic variation to phenotypic effects remains an open challenge in the field. This thesis attempts to address these three challenges via the development and application of statistical and deep learning approaches to mine large genomic, molecular and phenotypic datasets. The MyHeart Counts study serves as an example of how wearable and mobile technologies enable unobtrusive real-time measurements of complex phenotypes such as exercise and physical activity patterns. These technologies also enable rapid recruitment of large study cohorts and facilitate fully digital randomized controlled trials with low barriers to entry. Such technologies also facilitate the compilation of population-level biobanks, such as the UK Biobank by enabling acquisition of lifestyle and activity data at scale. Having acquired complex phenotypes on large data cohorts, we can begin to investigate the effects of genomic variation on these phenotypes by performing genomewide association studies (GWAS). Functional GWAS SNPs can be identified via in silico interrogation of predictive deep learning models of regulatory DNA. Here, I present convolutional neural network models trained on genome-wide chromatin profiling experiments to interpret and finemap GWAS SNPs by leveraging their ability to learn predictive DNA sequence syntax. Case studies in colorectal cancer and Alzheimer's disease are presented to illustrate the application of these methods. To improve the model stability and interpretability, I developed deep learning models that can predict regulatory chromatin profiles at single base resolution, accounting and correcting for confounding experimental biases. I also contributed to several collaborative investigations of the molecular basis of complex cellular phenotypes. We identified the Sp1 regulatory protein as a key regulator of matrix stiffness and induction of tumorigenic phenotypes in mammary epithelium; the PI3K pathway as a key modulator of efficiency of stem cell differentiation and transcription factor networks that regulate murine muscle stem cell aging through differentiation. In summary, this thesis presents new computational approaches for linking genotype to phenotype through mechanistic molecular mechanisms.
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 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 Scalable and Robust Statistical Inference Algorithms for Linking Genotypes to Phenotypes by : Ali Pazokitoroudi
Download or read book Scalable and Robust Statistical Inference Algorithms for Linking Genotypes to Phenotypes written by Ali Pazokitoroudi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advancements in DNA sequencing technology and the decreasing cost of sequencing, there has been exponential growth in the amount of genomic data generated. This growth provides an unprecedented opportunity to access the genotypes of a large population, including millions of genetic variants, and to collect hundreds of thousands of phenotypic measurements from the same individuals. This opens doors to systematically studying the genetic architecture underlying complex traits and diseases. Genetic architecture refers broadly to a complete understanding of all genetic contributions to a given trait as well as to an awareness of the characteristics of this contribution. During the past decade, variance components analysis has emerged as a robust statistical framework for investigating the genetic architectures of complex traits. To gain accurate and innovative insights into genetic architecture, applying flexible variance component models to large-scale datasets is crucial. However, fitting such models necessitates the use of scalable algorithms. Common approaches for estimating variance components involve searching for parameter values that maximize the likelihood or the restricted maximum likelihood (REML). Despite several algorithmic advancements, computing REML estimates of variance components on extensive datasets like the UK Biobank, which consists of approximately 500,000 genotyped individuals, millions of single nucleotide polymorphisms (SNPs), and hundreds of thousands of phenotypes, remains challenging. This thesis introduces a set of scalable and robust statistical inference algorithms rooted in variance component analysis. These algorithms are designed to estimate the variation in a trait that can be explained by linear and non-linear functions of the genotype, such as the interaction between alleles at a single genetic variant (dominance), the interaction between genetic variants (epistasis), and the interaction between environmental factors and genetic variants (GxE). Furthermore, these algorithms aim to estimate the distribution of these effects across the genome. By applying our methods to the UK Biobank dataset, we uncover valuable insights into the genetic architecture of complex traits. Notable observations are as follows. First, we observe that both per-allele squared additive and GxE effect size increase with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD). Second, testing whether GxE heritability is enriched around genes that are highly expressed in specific tissues, we find significant tissue-specific enrichments that include brain-specific enrichment for BMI and Basal Metabolic Rate in the context of smoking, adipose-specific enrichment for WHR in the context of sex, and cardiovascular tissue-specific enrichment for total cholesterol in the context of age. Third, we detect epistasis effects between SNPs located on the same chromosome and between SNPs located on different chromosomes. Fourth, our analyses indicate a limited contribution of dominance heritability to complex trait variation.
Book Synopsis Machine Learning and Network-Driven Integrative Genomics by : Mehdi Pirooznia
Download or read book Machine Learning and Network-Driven Integrative Genomics written by Mehdi Pirooznia and published by Frontiers Media SA. This book was released on 2021-04-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sparse Model Learning for Identifying Nucleotide Motifs and Inferring Genotype and Phenotype Associations by : Indika Priyantha Kuruppu Appuhamilage
Download or read book Sparse Model Learning for Identifying Nucleotide Motifs and Inferring Genotype and Phenotype Associations written by Indika Priyantha Kuruppu Appuhamilage and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variation in gene expression is an important mechanism underlying phenotypic variation in morphological, physiological and behavioral traits as well as disease susceptibility. A connection between DNA variants and gene expression levels not only provides more understanding of the biological network, but also enhances the mapping of these quantitative traits. Thus, an understanding of the mechanism of gene expression and the genotype/phenotype relationship is of paramount importance to both scientific research and social economics. The primary functionality of the gene expression process is to convert information stored in genes into gene products such as RNAs or proteins. The fundamental of this complex process is controlled by a class of proteins known as transcription factors (TFs) that bind to special locations of the DNA double helix. These special binding sites, known as transcription factor binding sites (TFBSs), are generally short motifs of 6-20 base pairs. Furthermore, the discovery of new TFBSs will contribute to the establishment of gene regulation networks, diagnosis of genetic diseases and new drug design. On the other hand, the genotype/phenotype relationship is mainly explained by multiple quantitative trait loci (QTLs), epistatic effects and environmental factors. A QTL is a section of DNA that correlates with variation in a phenotype. The QTL typically is linked to, or contains, the genes that control that phenotype interactions among QTLs or between genes, and environmental factors contribute substantially to variation in complex traits. During the last two decades the use of QTLs has proven to be effective for increasing food production, resistance to diseases and pests, tolerance to heat, cold and draught, and to improve nutrient content in animal and plant breeding. Therefore, the objective of this dissertation is to develop sparse models for such high dimensional data, develop accurate sparse variable selection and estimation algorithms for the models and design statistical methods for robust hypothesis tests for the TFBSs identification and QTL mapping problems. Although the sparse model learning works presented in this thesis are used in the context of TFBSs identification or QTL mapping problems, the algorithms are equally applicable to a broad range of problems, such as whole-genome QTL mapping and pathway-based genome-wide association study (GWAS), etc. The widely used computational methods for identifying TFBSs based on the position weight matrix (PWM) assume that the nucleotides at different positions of the TFBSs are independent. However, several experimental results demonstrate the dependencies among different positions. Recently, Bayesian networks (BN) and variable order Bayesian networks (VOBN) were proposed to model such dependencies and thereby improve the accuracy of predicting TFBSs. However, BN and VOBN model the dependencies in a directional manner, which may hinder their capability of completely capturing complex dependencies. To this end, we develop a Markov random field (MRF) based model for TFBSs capable of capturing complex unidirectional relationships among motifs. To capture the large extent of dependencies in a sparse model without causing overfitting, we develop a feature selection method that carefully chooses only the most relevant features of the model. An exhaustive simulation study affirmed that our MRF-based method outperforms other state-of-the-art methods based on VOBN. To further reduce the computational complexity of our algorithm, we introduce a novel pairwise MRF model to the TFBSs, and develop a fast algorithm to learn the model parameters. Specifically, we adopt an optimization method that employs the log determinant relaxation approach to evaluate the partition function in the MRF, which dramatically reduces the computational complexity of the algorithm. For the genotype/phenotype association problem, we develop a novel empirical Bayesian least absolute shrinkage and selection operator (EBlasso) algorithm with normal and exponential (NE) and normal, exponential and gamma (NEG) hierarchical prior distributions. Both of these algorithms employ a novel proximal gradient approach to simultaneously estimate model parameters that leads to extremely fast convergence. Furthermore, we develop a novel proximal gradient hybrid model capable of detecting more QTLs than its vanilla flavor, but still maintaining a lower false positive rate. Having both covariance and posterior modes estimated, they also provide a statistical testing method that considers as much information as possible without increasing the degrees of freedom (DF). Extensive simulation studies are carried out to evaluate the performance of the proposed methods, and real datasets are analyzed for validation. Both simulation and real data analyses suggest that the new methods are fast and accurate genotype-phenotype association methods that can easily handle high dimensional data, including possible main and interaction effects with orders of magnitude faster than existing state-of-the-art methods. Specifically, with the EBlasso-NEG, our new algorithm could easily handle more than [10]^5 possible effects within few seconds running on an average personal computer. Given the fundamental importance of gene expression and genotype/phenotype associations in understanding the genetic basis of complex biological system, the MRF, pairwise-MRF, EBlasso-NE, EBlasso-NEG and EBlasso-NEG hybrid algorithms and software packages developed in this dissertation achieve the effectiveness, robustness and efficiency needed for successful application to biology. With the advancement of high-throughput molecular technologies in generating information at genetic, epigenetic, transcriptional and posttranscriptional levels, the methods developed here have broad applications to infer TFBSs and different types of genotype and phenotypes associations.
Book Synopsis Machine Learning Approaches for Phenotype Refinement to Improve Genetic Association Analysis by :
Download or read book Machine Learning Approaches for Phenotype Refinement to Improve Genetic Association Analysis written by and published by . This book was released on 2015 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis From Genotype to Phenotype: Inferring Relationships Between Microbial Traits and Genomic Components by : Aaron Weimann
Download or read book From Genotype to Phenotype: Inferring Relationships Between Microbial Traits and Genomic Components written by Aaron Weimann and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision medicine is being developed as a preventative, diagnostic and treatment tool to combat complex human diseases in a personalized manner. By utilizing high-throughput technologies, dynamic 'omics data including genetics, epi-genetics and even meta-genomics has produced temporal-spatial big biological datasets which can be associated with individual genotypes underlying pathogen progressive phenotypes. It is therefore necessary to investigate how to integrate these multi-scale 'omics datasets to distinguish the novel individual-specific disease causes from conventional cohort-common disease causes. Currently, machine learning plays an important role in biological and biomedical research, especially in the analysis of big 'omics data. However, in contrast to traditional big social data, 'omics datasets are currently always "small-sample-high-dimension", which causes overwhelming application problems and also introduces new challenges: (1) Big 'omics datasets can be extremely unbalanced, due to the difficulty of obtaining enough positive samples of such rare mutations or rare diseases; (2) A large number of machine learning models are "black box," which is enough to apply in social applications. However, in biological or biomedical fields, knowledge of the molecular mechanisms underlying any disease or biological study is necessary to deepen our understanding; (3) The genotype-phenotype association is a "white clue" captured in conventional big data studies. But identification of "causality" rather than association would be more helpful for physicians or biologists, as this can be used to determine an experimental target as the subject of future research. Therefore, to simultaneously improve the phenotype discrimination and genotype interpretability for complex diseases, it is necessary: To design and implement new machine learning technologies to integrate prior-knowledge with new 'omics datasets to provide transferable learning methods by combining multiple sources of data; To develop new network-based theories and methods to balance the trade-off between accuracy and interpretability of machine learning in biomedical and biological domains; To enhance the causality inference on "small-sample high dimension" data to capture the personalized causal relationship.
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 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.
