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Computational Methods For The Analysis Of Genomic Data And Biological Processes

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Computational Methods For The Analysis Of Genomic Data And Biological Processes

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Author : Francisco A. Gómez Vela
language : en
Publisher: MDPI
Release Date : 2021-02-05

Computational Methods For The Analysis Of Genomic Data And Biological Processes written by Francisco A. Gómez Vela and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-05 with Medical categories.

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

Computational Methods For The Analysis Of Genomic Data And Biological Processes

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Author : Francisco A. Gómez Vela
language : en
Publisher:
Release Date : 2021

Computational Methods For The Analysis Of Genomic Data And Biological Processes written by Francisco A. Gómez Vela and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Biological Data Analysis And Computational Methods

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Author : Mr. Rohit Manglik
language : en
Publisher: EduGorilla Publication
Release Date : 2024-03-26

Biological Data Analysis And Computational Methods written by Mr. Rohit Manglik and has been published by EduGorilla Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-26 with Science categories.

EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels.

Computational Genomics With R

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Author : Altuna Akalin
language : en
Publisher: CRC Press
Release Date : 2020-12-16

Computational Genomics With R written by Altuna Akalin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-16 with Mathematics categories.

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.

Analysis Of Biological Data

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Author : Sanghamitra Bandyopadhyay
language : en
Publisher: World Scientific
Release Date : 2007

Analysis Of Biological Data written by Sanghamitra Bandyopadhyay and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.

Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). Contents: Overview: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim); An Introduction to Soft Computing (A Konar & S Das); Biological Sequence and Structure Analysis: Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.); Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu); Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li); Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa); Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.); In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.); Gene Expression and Microarray Data Analysis: Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba); A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.); Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.); Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park); Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries.

Catalyzing Inquiry At The Interface Of Computing And Biology

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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2006-01-01

Catalyzing Inquiry At The Interface Of Computing And Biology written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-01 with Science categories.

Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies.

Computational Methods With Applications In Bioinformatics Analysis

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Author : Jeffrey J P Tsai
language : en
Publisher: World Scientific
Release Date : 2017-06-09

Computational Methods With Applications In Bioinformatics Analysis written by Jeffrey J P Tsai and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-09 with Science categories.

This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.

Computational Intelligence For Genomics Data

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Author : Babita Pandey
language : en
Publisher: Elsevier
Release Date : 2025-01-21

Computational Intelligence For Genomics Data written by Babita Pandey and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-21 with Computers categories.

Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers. - Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing major design challenges, best practices, pitfalls, and research potential - Explores machine and deep learning techniques applied to dimensionality reduction, feature extraction, data selection, and their application in genomics - Presents case studies of various diseases based on gene microarray expression data, including cancer, liver disorders, neuromuscular disorders, and neurodegenerative disorders

Computational Methods For Single Cell Data Analysis

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Author : Guo-Cheng Yuan
language : en
Publisher: Humana Press
Release Date : 2019-02-14

Computational Methods For Single Cell Data Analysis written by Guo-Cheng Yuan and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-14 with Science categories.

This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

Computational Methods In Medicinal Chemistry Pharmacology And Toxicology

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Author : Muhammad Ishfaq
language : en
Publisher: Academic Press
Release Date : 2025-06-13

Computational Methods In Medicinal Chemistry Pharmacology And Toxicology written by Muhammad Ishfaq and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-13 with Medical categories.

Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology is a comprehensive resource that offers an advanced overview of computational techniques employed in drug discovery, design, and toxicity prediction. The book discusses various topics, including molecular modeling, virtual screening, machine learning, and network pharmacology. It serves as an essential guide for researchers, practitioners, and students in pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology fields, showcasing practical applications and future perspectives on new technologies. In addition to covering computational approaches, the book provides real-world examples of drug discovery, candidate optimization, and safety assessment.Other sections explore computer applications in pharmacology and toxicology and discusses the importance of these methods in advancing medicinal research. - Offers comprehensive coverage of computational methods that are relevant to pharmacology and toxicology, including molecular modeling, virtual screening, machine learning, and network pharmacology - Includes practical examples and case studies that demonstrate how these methods can be applied in drug discovery, design, and toxicity prediction - Discusses emerging trends and future directions in the field of computational pharmacology and toxicology that can help readers stay up-to-date with the latest advances and anticipate future developments

Computational Methods For The Analysis Of Genomic Data And Biological Processes

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