Machine Learning Techniques on Gene Function Prediction
Author: Quan Zou
Publisher: Frontiers Media SA
Published: 2019-12-04
Total Pages: 485
ISBN-13: 2889632148
DOWNLOAD EBOOKPosts
Machine Learning Techniques on Gene Function Prediction Volume II
Author: Quan Zou
Publisher: Frontiers Media SA
Published: 2023-04-11
Total Pages: 264
ISBN-13: 2889766322
DOWNLOAD EBOOKKnowledge and Systems Engineering
Author: Viet-Ha Nguyen
Publisher: Springer
Published: 2014-09-29
Total Pages: 673
ISBN-13: 3319116800
DOWNLOAD EBOOKThis volume contains papers presented at the Sixth International Conference on Knowledge and Systems Engineering (KSE 2014), which was held in Hanoi, Vietnam, during 9–11 October, 2014. The conference was organized by the University of Engineering and Technology, Vietnam National University, Hanoi. Besides the main track of contributed papers, this proceedings feature the results of four special sessions focusing on specific topics of interest and three invited keynote speeches. The book gathers a total of 51 carefully reviewed papers describing recent advances and development on various topics including knowledge discovery and data mining, natural language processing, expert systems, intelligent decision making, computational biology, computational modeling, optimization algorithms, and industrial applications.
The Gene Ontology Handbook
Author: Christophe Dessimoz
Publisher:
Published: 2020-10-08
Total Pages: 298
ISBN-13: 9781013267710
DOWNLOAD EBOOKThis book provides a practical and self-contained overview of the Gene Ontology (GO), the leading project to organize biological knowledge on genes and their products across genomic resources. Written for biologists and bioinformaticians, it covers the state-of-the-art of how GO annotations are made, how they are evaluated, and what sort of analyses can and cannot be done with the GO. In the spirit of the Methods in Molecular Biology book series, there is an emphasis throughout the chapters on providing practical guidance and troubleshooting advice. Authoritative and accessible, The Gene Ontology Handbook serves non-experts as well as seasoned GO users as a thorough guide to this powerful knowledge system. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Data Mining in Bioinformatics
Author: Jason T. L. Wang
Publisher: Springer Science & Business Media
Published: 2005
Total Pages: 356
ISBN-13: 9781852336714
DOWNLOAD EBOOKWritten especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.
Machine Learning Meets Quantum Physics
Author: Kristof T. Schütt
Publisher: Springer Nature
Published: 2020-06-03
Total Pages: 473
ISBN-13: 3030402452
DOWNLOAD EBOOKDesigning molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Biological Sequence Analysis
Author: Richard Durbin
Publisher: Cambridge University Press
Published: 1998-04-23
Total Pages: 372
ISBN-13: 113945739X
DOWNLOAD EBOOKProbabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
Introduction to Protein Structure Prediction
Author: Huzefa Rangwala
Publisher: John Wiley & Sons
Published: 2011-03-16
Total Pages: 611
ISBN-13: 111809946X
DOWNLOAD EBOOKA look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Big Data Analytics in Genomics
Author: Ka-Chun Wong
Publisher: Springer
Published: 2016-10-24
Total Pages: 426
ISBN-13: 3319412795
DOWNLOAD EBOOKThis 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.
Tumor Microenvironment and Cellular Stress
Author: Constantinos Koumenis
Publisher: Springer Science & Business Media
Published: 2013-11-23
Total Pages: 293
ISBN-13: 146145915X
DOWNLOAD EBOOKThe collection of chapters in this proceeding volume reflects the latest research presented at the Aegean meeting on Tumor Microenvironment and Cellular Stress held in Crete in Fall of 2012. The book provides critical insight to how the tumor microenvironment affects tumor metabolism, cell stemness, cell viability, genomic instability and more. Additional topics include identifying common pathways that are potential candidates for therapeutic intervention, which will stimulate collaboration between groups that are more focused on elucidation of biochemical aspects of stress biology and groups that study the pathophysiological aspects of stress pathways or engaged in drug discovery.
