Download or read book Machine Learning for Large scale Genomics written by Yifei Chen and published by . This book was released on 2014 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic malformations are believed to be the driving factors of many diseases. Therefore, understanding the intrinsic mechanisms underlying the genome and informing clinical practices have become two important missions of large-scale genomic research. Recently, high-throughput molecular data have provided abundant information about the whole genome, and have popularized computational tools in genomics. However, traditional machine learning methodologies often suffer from strong limitations when dealing with high-throughput genomic data, because the latter are usually very high dimensional, highly heterogeneous, and can show complicated nonlinear effects. In this thesis, we present five new algorithms or models to address these challenges, each of which is applied to a specific genomic problem. Project 1 focuses on model selection in cancer diagnosis. We develop an efficient algorithm (ADMM-ENSVM) for the Elastic Net Support Vector Machine, which achieves simultaneous variable selection and max-margin classification. On a colon cancer diagnosis dataset, ADMM-ENSVM shows advantages over other SVM algorithms in terms of diagnostic accuracy, feature selection ability, and computational efficiency. Project 2 focuses on model selection in gene correlation analysis. We develop an efficient algorithm (SBLVGG) using the similar methodology as of ADMM-ENSVM for the Latent Variable Gaussian Graphical Model (LVGG). LVGG models the marginal concentration matrix of observed variables as a combination of a sparse matrix and a low rank one. Evaluated on a microarray dataset containing 6,316 genes, SBLVGG is notably faster than the state-of-the-art LVGG solver, and shows that most of the correlation among genes can be effectively explained by only tens of latent factors. Project 3 focuses on ensemble learning in cancer survival analysis. We develop a gradient boosting model (GBMCI), which does not explicitly assume particular forms of hazard functions, but trains an ensemble of regression trees to approximately optimize the concordance index. We benchmark the performance of GBMCI against several popular survival models on a large-scale breast cancer prognosis dataset. GBMCI consistently outperforms other methods based on a number of feature representations, which are heterogeneous and contain missing values. Project 4 focuses on deep learning in gene expression inference (GEIDN). GEIDN is a large-scale neural network, which can infer ~21k target genes jointly from ~1k landmark genes and can naturally capture hierarchical nonlinear interactions among genes. We deploy deep learning techniques (drop out, momentum training, GPU computing, etc.) to train GEIDN. On a dataset of ~129k complete human transcriptomes, GEIDN outperforms both k-nearest neighbor regression and linear regression in predicting >99.96% of the target genes. Moreover, increased network scales help to improve GEIDN, while increased training data benefits GEIDN more than other methods. Project 5 focuses on deep learning in annotating coding and noncoding genetic variants (DANN). DANN is a neural network to differentiate evolutionarily derived alleles from simulated ones with 949 highly heterogeneous features. It can capture nonlinear relationships among features. We train DANN with deep learning techniques like for GEIDN. DANN achieves a 18.90% relative reduction in the error rate and a 14.52% relative increase in the area under the curve over CADD, a state-of-the-art algorithm to annotate genetic variants based on the linear SVM.

Download or read book Handbook of Machine Learning Applications for Genomics written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2022-06-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.

Download or read book Machine Learning Methods for Multi Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.

Download or read book Machine Learning in Genome Wide Association Studies written by Ting Hu and published by Frontiers Media SA. This book was released on 2020-12-15 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Download or read book Efficient Large Scale Machine Learning Algorithms for Genomic Sequences written by Daniel Quang and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing (HTS) has led to many breakthroughs in basic and translational biology research. With this technology, researchers can interrogate whole genomes at single-nucleotide resolution. The large volume of data generated by HTS experiments necessitates the development of novel algorithms that can efficiently process these data. At the advent of HTS, several rudimentary methods were proposed. Often, these methods applied compromising strategies such as discarding a majority of the data or reducing the complexity of the models. This thesis focuses on the development of machine learning methods for efficiently capturing complex patterns from high volumes of HTS data.First, we focus on on de novo motif discovery, a popular sequence analysis method that predates HTS. Given multiple input sequences, the goal of motif discovery is to identify one or more candidate motifs, which are biopolymer sequence patterns that are conjectured to have biological significance. In the context of transcription factor (TF) binding, motifs may represent the sequence binding preference of proteins. Traditional motif discovery algorithms do not scale well with the number of input sequences, which can make motif discovery intractable for the volume of data generated by HTS experiments. One common solution is to only perform motif discovery on a small fraction of the sequences. Scalable algorithms that simplify the motif models are popular alternatives. Our approach is a stochastic method that is scalable and retains the modeling power of past methods.Second, we leverage deep learning methods to annotate the pathogenicity of genetic variants. Deep learning is a class of machine learning algorithms concerned with deep neural networks (DNNs). DNNs use a cascade of layers of nonlinear processing units for feature extraction and transformation. Each layer uses the output from the previous layer as its input. Similar to our novel motif discovery algorithm, artificial neural networks can be efficiently trained in a stochastic manner. Using a large labeled dataset comprised of tens of millions of pathogenic and benign genetic variants, we trained a deep neural network to discriminate between the two categories. Previous methods either focused only on variants lying in protein coding regions, which cover less than 2% of the human genome, or applied simpler models such as linear support vector machines, which can not usually capture non-linear patterns like deep neural networks can.Finally, we discuss convolutional (CNN) and recurrent (RNN) neural networks, variations of DNNs that are especially well-suited for studying sequential data. Specifically, we stacked a bidirectional recurrent layer on top of a convolutional layer to form a hybrid model. The model accepts raw DNA sequences as inputs and predicts chromatin markers, including histone modifications, open chromatin, and transcription factor binding. In this specific application, the convolutional kernels are analogous to motifs, hence the model learning is essentially also performing motif discovery. Compared to a pure convolutional model, the hybrid model requires fewer free parameters to achieve superior performance. We conjecture that the recurrent layer allows our model spatial and orientation dependencies among motifs better than a pure convolutional model can. With some modifications to this framework, the model can accept cell type-specific features, such as gene expression and open chromatin DNase I cleavage, to accurately predict transcription factor binding across cell types. We submitted our model to the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, where it was among the top performing models. We implemented several novel heuristics, which significantly reduced the training time and the computational overhead. These heuristics were instrumental to meet the Challenge deadlines and to make the method more accessible for the research community.HTS has already transformed the landscape of basic and translational research, proving itself as a mainstay of modern biological research. As more data are generated and new assays are developed, there will be an increasing need for computational methods to integrate the data to yield new biological insights. We have only begun to scratch the surface of discovering what is possible from both an experimental and a computational perspective. Thus, further development of versatile and efficient statistical models is crucial to maintaining the momentum for new biological discoveries.

Download or read book Artificial Intelligence written by and published by BoD – Books on Demand. This book was released on 2019-07-31 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.

Download or read book Genomics Assisted Crop Improvement written by R.K. Varshney and published by Springer Science & Business Media. This book was released on 2007-12-12 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This superb volume provides a critical assessment of genomics tools and approaches for crop breeding. Volume 1 presents the status and availability of genomic resources and platforms, and also devises strategies and approaches for effectively exploiting genomics research. Volume 2 goes into detail on a number of case studies of several important crop and plant species that summarize both the achievements and limitations of genomics research for crop improvement.

Download or read book Leveraging Big Data and Machine Learning Technologies for Accurate and Scalable Genomic Analysis written by Lizhen Shi and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data and machine learning technologies have been explored to mine the complex large-scale genomics data. In this dissertation, we first survey some of the existing scalable approaches for genomic analysis and identify the limitations of these solutions. We then investigate the still-unsolved challenges faced by computational biologists in large-scale genomic analysis. Specifically, in terms of using MapReduce-based bioinformatics analysis tools, Hadoop has a large number of parameters to control the behavior of a MapReduce job. The unique characteristics of MapReduce-based bioinformatics tools makes all the existing guidelines inapplicable; In Metagenomics, the intrinsic complexity and massive quantity of metagenomic data create tremendous challenges for microbial genomes recovery; When we applying NLP technologies to genome analysis, the enormous k-mer size and the low-frequency k-mers caused by the sequencing errors post significant challenges for k-mer embedding. To overcome the aforementioned problems, this dissertation introduces three countermeasures. First, we extract the key parameters from the large space of MapReduce parameters and present an exemplary case for tuning MapReduce-based bioinformatics analysis tools based on their unique characteristics. Second, we design and implement SpaRC, a scalable sequence clustering tool built on Apache Spark, to partition reads based on their molecules of origin to enable downstream assembly optimization in Metagenomics. SpaRC achieves high clustering accuracy, with the capability of scaling near linearly with the data size and the number of computing nodes. Lastly, we leverage Locality Sensitive Hashing (LSH) to overcome the two challenges faced by $k$-mer embedding and design LSHvec. With LSHvec, a DNA sequence can be represented as a dense low-dimensional vector. The trained sequence vectors are capable of capturing the rich characteristics of DNA sequences and can be fed to machine learning models for a wide variety of applications in genomics analysis. We compare our approaches with existing solutions. The experiments demonstrate our approaches achieve the state-of-the-art results. We open source our implementation of SpaRC and LSHvec to facilitate comparison of future work and inspire future research in genomic analysis.

Download or read book Deep Learning for Genomics written by Upendra Kumar Devisetty and published by Packt Publishing Ltd. This book was released on 2022-11-11 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.

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.

Download or read book Genomics in the Cloud written by Geraldine A. Van der Auwera and published by O'Reilly Media. This book was released on 2020-04-02 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra

Download or read book Computational Methods for Processing and Analyzing Large Scale Genomics Datasets written by Olivera Grujic and published by . This book was released on 2016 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops computational methods for analyzing large-scale genomic and epigenomic datasets. We developed a supervised machine learning approach to predict non-exonic evolutionarily conserved regions in the human genome based on vast amount of functional genomics data. The resulting probabilistic predictions provide a resource for prioritizing functionally important regulatory regions in the human genome. We also developed a method for identifying from large-scale gene expression datasets genes that are differentially expressed in both blood and brain from 12 vervet monkeys, which we used to identify 29 transcripts whose expression is variable between individuals and heritable. Additionally, we developed a method using a global search optimization algorithm to successfully improve a model of human thyroid hormone regulation dynamics leading to a better fit of data for thyrotoxicosis. Together, these three approaches have the potential to impact the understanding and eventual treatment of disease.

Download or read book Biologically Informed Feature Selection in Large Scale Genomics written by William Stone and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive genetics is a promising field of research, particularly in medical science where the ability to identify disease or treatment response could provide novel methods of mitigating their negative effects. Machine learning represents the most obvious tool that can be used to this end, however a notable property of genetic data that proves difficult for machine learning is a significant imbalance between samples and features, indicating the need for feature selection. The dataset we used was collected from multiple international centres and includes subjects with bipolar disorder, some of whom respond to the drug lithium and some who do not. We first select the features that were measured jointly by each data collection centre and show that above chance classification is possible with these data, despite significant overfitting which indicated the need for further feature space reduction. We then introduce a novel method capable of reducing the number of features even further so as to be bounded by the number of subjects. This method uses the hierarchical structure of genetic data to select feature subsets and evaluate their fitness individually before including the best ones in the final feature set. We show that our method improves on the first method while maintaining biological interpretability.

Download or read book Big Data Analytics in Genomics written by Ka-Chun Wong and published by Springer. This book was released on 2016-10-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Download or read book Genomic Biointelligence written by Edenilson Brandl and published by Edenilson Brandl. This book was released on with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is with great enthusiasm that I present to you the book "Genomic Biointelligence". This book is a fascinating journey through the ever-evolving world of genomics and artificial intelligence, exploring their intersection and the role of the genomic biointelligence within this context. Genomics has revolutionized our understanding of the genetic code and brought with it a vast volume of data that challenges our ability to analyze and interpret. On the other hand, artificial intelligence has emerged as a powerful tool to deal with this complexity and extract valuable information from genomic data. Within the pages of this book, you will be guided on a comprehensive journey through key topics related to the application of artificial intelligence in genomics. From the history and evolution of artificial intelligence in genomics research to the latest applications in diagnostics, drug discovery, precision medicine and disease research, each chapter presents an important aspect of this rapidly expanding field. You will learn about genetic algorithms and their application in genomics, mathematical modeling of genomic regulatory networks, the use of neural networks in predicting protein structures, and much more. We will also discuss the challenges and limitations of using artificial intelligence in genomics, as well as ethical issues and the importance of data privacy. In addition, we will highlight the fundamental role of the genomic biointelligencist, a multidisciplinary professional who combines knowledge in genomics, artificial intelligence, bioinformatics and other related areas. The genomic biointelligence plays a crucial role in applying artificial intelligence to advance genomic research, discover new treatments, develop personalized therapies, and drive precision medicine. As we progress through this book, you will be invited to explore recent advances and the exciting possibilities that arise from the combination of genomics and artificial intelligence. Through practical examples, case studies and in-depth discussions, we hope to provide you with a solid understanding of the concepts and applications of this rapidly expanding field. Finally, I would like to express my gratitude to all the experts and researchers who contributed their unique knowledge and insights to this book. Their efforts and dedication are instrumental in advancing the field of genomics and artificial intelligence. I hope you will find this book a valuable source of information and inspiration. May it arouse your curiosity, stimulate discussions and motivate you to further explore the frontiers of knowledge in the field of genomics and artificial intelligence.

Download or read book Next Steps for Functional Genomics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-18 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.