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Author : Quan Zou
Publisher : Frontiers Media SA
ISBN 13 : 2832516017
Total Pages : 150 pages
Book Rating : 4.8/5 (325 download)
Download or read book Machine learning for biological sequence analysis written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-03-09 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Richard Durbin
Publisher : Cambridge University Press
ISBN 13 : 113945739X
Total Pages : 372 pages
Book Rating : 4.1/5 (394 download)
Download or read book Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic 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.
Author : Pierre Baldi
Publisher : MIT Press
ISBN 13 : 9780262025065
Total Pages : 492 pages
Book Rating : 4.0/5 (25 download)
Download or read book Bioinformatics, second edition written by Pierre Baldi and published by MIT Press. This book was released on 2001-07-20 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
Author : Lloyd Wai Yee Low
Publisher : World Scientific
ISBN 13 : 9811259003
Total Pages : 268 pages
Book Rating : 4.8/5 (112 download)
Download or read book Practical Bioinformatics For Beginners: From Raw Sequence Analysis To Machine Learning Applications written by Lloyd Wai Yee Low and published by World Scientific. This book was released on 2023-01-17 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next-Generation Sequencing (NGS) is increasingly common and has applications in various fields such as clinical diagnosis, animal and plant breeding, and conservation of species. This incredible tool has become cost-effective. However, it generates a deluge of sequence data that requires efficient analysis. The highly sought-after skills in computational and statistical analyses include machine learning and, are essential for successful research within a wide range of specializations, such as identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine, and increased crop yields in agriculture.This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform specific analyses, from raw sequenced data to answer important biological questions using machine learning methods. It is an excellent hands-on material for lecturers who conduct courses in bioinformatics and as reference material for professionals. The chapters are standalone recipes making them suitable for readers who wish to self-learn selected topics. Readers gain the essential skills necessary to work on sequenced data from NGS platforms; hence, making themselves more attractive to employers who need skilled bioinformaticians.
Author : Stephen Winters-Hilt
Publisher : Lulu.com
ISBN 13 : 1257645250
Total Pages : 436 pages
Book Rating : 4.2/5 (576 download)
Download or read book MacHine-Learning Based Sequence Analysis, Bioinformatics and Nanopore Transduction Detection written by Stephen Winters-Hilt and published by Lulu.com. This book was released on 2011-05-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.
Author : Columbia University. Dept. of Computer Science
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (268 download)
Download or read book Machine Learning in Molecular Biology Sequence Analysis written by Columbia University. Dept. of Computer Science and published by . This book was released on 1991 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "To investigate how human characteristics are inherited, molecular biologists have been analyzing chemical sequences from DNA, RNA, and proteins. To facilitate this process, sequence analysis knowledge has been encoded in computer programs. However, translating human knowledge to programs is known to be problematic. Machine Learning techniques allow these systems to be generated automatically. This article discusses the application of learning techniques to various analysis tasks. It is shown that the learned systems constructed to date are often more accurate than human-designed systems. Moreover, learning can form plausible new hypotheses, which potentially lead to discovering new knowledge."
Author : Sushmita Mitra
Publisher : CRC Press
ISBN 13 : 1420011782
Total Pages : 386 pages
Book Rating : 4.4/5 (2 download)
Download or read book Introduction to Machine Learning and Bioinformatics written by Sushmita Mitra and published by CRC Press. This book was released on 2008-06-05 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.
Author : Alex Graves
Publisher : Springer
ISBN 13 : 3642247970
Total Pages : 148 pages
Book Rating : 4.6/5 (422 download)
Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by Alex Graves and published by Springer. This book was released on 2012-02-06 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
Author : Sanghamitra Bandyopadhyay
Publisher : World Scientific
ISBN 13 : 9812708898
Total Pages : 353 pages
Book Rating : 4.8/5 (127 download)
Download or read book Analysis of Biological Data written by Sanghamitra Bandyopadhyay and published by World Scientific. This book was released on 2007 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Martin J. Bishop
Publisher : Oxford University Press, USA
ISBN 13 :
Total Pages : 446 pages
Book Rating : 4.3/5 (91 download)
Download or read book Nucleic Acid and Protein Sequence Analysis written by Martin J. Bishop and published by Oxford University Press, USA. This book was released on 1987 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Faheem Masoodi
Publisher : CRC Press
ISBN 13 : 1000833798
Total Pages : 233 pages
Book Rating : 4.0/5 (8 download)
Download or read book Applications of Machine Learning and Deep Learning on Biological Data written by Faheem Masoodi and published by CRC Press. This book was released on 2023-03-13 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms. Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment. Highlights include: Artificial Intelligence in treating and diagnosing schizophrenia An analysis of ML’s and DL’s financial effect on healthcare An XGBoost-based classification method for breast cancer classification Using ML to predict squamous diseases ML and DL applications in genomics and proteomics Applying ML and DL to biological data
Author : K. G. Srinivasa
Publisher : Springer Nature
ISBN 13 : 9811524459
Total Pages : 318 pages
Book Rating : 4.8/5 (115 download)
Download or read book Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications written by K. G. Srinivasa and published by Springer Nature. This book was released on 2020-01-30 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Author : Andreas Gogol-Döring
Publisher : CRC Press
ISBN 13 : 1420076248
Total Pages : 330 pages
Book Rating : 4.4/5 (2 download)
Download or read book Biological Sequence Analysis Using the SeqAn C++ Library written by Andreas Gogol-Döring and published by CRC Press. This book was released on 2009-11-11 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Easy-to-Use Research Tool for Algorithm Testing and DevelopmentBefore the SeqAn project, there was clearly a lack of available implementations in sequence analysis, even for standard tasks. Implementations of needed algorithmic components were either unavailable or hard to access in third-party monolithic software products. Addressing these conc
Author : Pierre Baldi
Publisher : MIT Press (MA)
ISBN 13 : 9780262024426
Total Pages : 351 pages
Book Rating : 4.0/5 (244 download)
Download or read book Bioinformatics written by Pierre Baldi and published by MIT Press (MA). This book was released on 1998 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
Author : Yanqing Zhang
Publisher : John Wiley & Sons
ISBN 13 : 0470397411
Total Pages : 476 pages
Book Rating : 4.4/5 (73 download)
Download or read book Machine Learning in Bioinformatics written by Yanqing Zhang and published by John Wiley & Sons. This book was released on 2009-02-23 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Author : Lukasz Kurgan
Publisher : World Scientific
ISBN 13 : 9811258597
Total Pages : 378 pages
Book Rating : 4.8/5 (112 download)
Download or read book Machine Learning In Bioinformatics Of Protein Sequences: Algorithms, Databases And Resources For Modern Protein Bioinformatics written by Lukasz Kurgan and published by World Scientific. This book was released on 2022-12-06 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Bioinformatics of Protein Sequences guides readers around the rapidly advancing world of cutting-edge machine learning applications in the protein bioinformatics field. Edited by bioinformatics expert, Dr Lukasz Kurgan, and with contributions by a dozen of accomplished researchers, this book provides a holistic view of the structural bioinformatics by covering a broad spectrum of algorithms, databases and software resources for the efficient and accurate prediction and characterization of functional and structural aspects of proteins. It spotlights key advances which include deep neural networks, natural language processing-based sequence embedding and covers a wide range of predictions which comprise of tertiary structure, secondary structure, residue contacts, intrinsic disorder, protein, peptide and nucleic acids-binding sites, hotspots, post-translational modification sites, and protein function. This volume is loaded with practical information that identifies and describes leading predictive tools, useful databases, webservers, and modern software platforms for the development of novel predictive tools.
Author : Gunnar von Heijne
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.3/5 (91 download)
Download or read book Sequence Analysis in Molecular Biology written by Gunnar von Heijne and published by . This book was released on 1987 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sequence Analysis in Molecular Biology ...
