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.

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.

Download or read book Informatics and Machine Learning written by Stephen Winters-Hilt and published by John Wiley & Sons. This book was released on 2022-01-06 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Informatics and Machine Learning Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work. The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author’s teaching and industry experience. A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes’ rule An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

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.

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:

Download or read book Nanopore Sequencing written by Kazuharu Arakawa and published by Springer Nature. This book was released on 2023-02-14 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides comprehensive dry and wet experiments, methods, and applications on nanopore sequencing. Chapters guide readers through bioinformatic procedures, genome sequencing, analysis of repetitive regions, structural variations, rapid and on-site microbial identification, epidemiology, and transcriptome analysis. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Nanopore Sequencing: Methods and Protocols aims to be comprehensive guide for researchers.

Download or read book Machine Learning and Systems Biology in Genomics and Health written by Shailza Singh and published by Springer Nature. This book was released on 2022-02-04 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-09-12 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Download or read book Deep Learning Applications in Translational Bioinformatics written by Khalid Raza and published by Elsevier. This book was released on 2024-03 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Download or read book Pattern Recognition in Bioinformatics written by Jagath C.- Rajapakse and published by Springer Science & Business Media. This book was released on 2007-09-17 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2007, held in Singapore in October 2007. The 38 revised full papers presented were carefully reviewed and selected from 125 submissions. The papers discuss the applications of pattern recognition methods in the field of bioinformatics to solve problems in life sciences.

Download or read book Pattern Recognition in Bioinformatics written by Marco Loog and published by Springer. This book was released on 2011-10-29 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Conference on Pattern Recognition in Bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers cover the wide range of possible applications of bioinformatics in pattern recognition: novel algorithms to handle traditional pattern recognition problems such as (bi)clustering, classification and feature selection; applications of (novel) pattern recognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. They are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.

Download or read book Next Generation Sequencing Technologies and Challenges in Sequence Assembly written by Sara El-Metwally and published by Springer Science & Business. This book was released on 2014-04-19 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of Next Generation Sequencing (NGS) technologies resulted in a major transformation in the way scientists extract genetic information from biological systems, revealing limitless insight about the genome, transcriptome and epigenome of any species. However, with NGS, came its own challenges that require continuous development in the sequencing technologies and bioinformatics analysis of the resultant raw data and assembly of the full length genome and transcriptome. Such developments lead to outstanding improvements of the performance and coverage of sequencing and improved quality for the assembled sequences, nevertheless, challenges such as sequencing errors, expensive processing and memory usage for assembly and sequencer specific errors remains major challenges in the field. This book aims to provide brief overviews the NGS field with special focus on the challenges facing the NGS field, including information on different experimental platforms, assembly algorithms and software tools, assembly error correction approaches and the correlated challenges.

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 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique selling point: Advanced AI solutions for problems in genetics, virology, and related areas of life science Core audience: Researchers in bioinformatics Place in the market: High-level reference book on advanced applied technology

Download or read book Deep Sequencing Data Analysis written by Noam Shomron and published by . This book was released on 2021 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition provides new and updated chapters from expert researchers in the field detailing methods used to study the multi-facet deep sequencing data field. Chapters guide readers through techniques for processing RNA-seq data, microbiome analysis, deep learning methodologies, and various approaches for the identification of sequence variants. 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, Deep Sequencing Data Analysis: Methods and Protocols, Second Edition aims to ensure successful results in the further study of this vital field.

Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by Chapman & Hall/CRC. This book was released on 2021-08 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences

Download or read book Sequence Analysis Algorithms for Bioinformatics Application written by Mohamed Issa and published by . This book was released on 2014-10-06 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2014 in the subject Computer Science - Bioinformatics, grade: N, language: English, abstract: The data from next generation sequencing technologies has led to an explosion in genome sequence data available in public databases. This data provides unique opportunities to study the molecular mechanisms of gene evolution: how new genes and proteins originate and how they diversify. A major challenge is retracing origin of extant genes or proteins, by searching existing databases for related sequences and identifying evolutionary similarities. Therefore, enhanced and faster search algorithms are being developed, e.g. on accelerators such as GPU, in order to cope with the huge size of today's DNA or protein sequence databases. Gene-Tracer is a tool was developed to localize the common sub-sequences between two ancestors and its offspring. Besides, compute percentages of ancestors' contributions in offspring. Gene-Tracer was developed to find the origin of unknown shuffling/offspring sequence. A database is scanned and the similarity between offspring sequence and each one in the database is computed using pairwise local sequence alignment algorithm. Based on similarity score, 100 sequences that have the highest score is re-aligned with shuffling sequence to determine length of common sub-sequences between them using local alignment algorithm. The two sequences that have longest sub-sequences with shuffling are the nearest origin to offspring. Swiss-port database contains around 400,000 proteins is used in the test. The execution time around hours. So, GPU is to accelerate the tool. Speedup is 84x using single-GPU Tesla C2075 versus Intel(c) Core i3 multiprocessor. Finally, the main contribution of work is developing fast tool that re-trace origins of unknown gene/protein sequences."

Download or read book Improvements in Machine Learning for Predicting Taxon Phenotype and Function from Genetic Sequences written by Zhengqiao Zhao and published by . This book was released on 2020 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in DNA sequencing, as well as the rise of shotgun metagenomics and metabolomics, are rapidly producing complex microbiome datasets for studies of human health and the environment. The large-scale sampling of DNA/RNA from microbes provides a window into the microbiome's interactions with its host and habitat, enables us to predict phenotypic traits of the host/microbiome, aids the discovery of emergent biological function, and supports the medical diagnosis. Researchers try to extract features from DNA/RNA sequencing data and make 1) taxonomic predictions ("Who is there"), 2) function annotations ("What they are doing") and 3) host/microbiome phenotype predictions. This work is to explore different computational methods to address challenges in these three fields. First, taxonomic classification relies on NCBI RefSeq database sequences, which are being added at an exponential rate. Therefore, the incremental learning concept is especially important. Although the incremental naive Bayes classifier (NBC) is a decade old concept, it has not been applied to taxonomic classification in the metagenomics field. In this work, I compare the classification accuracy and runtime of the proposed incremental learning implementation of NBC with the performance of the traditional implementation of NBC and demonstrate a proof of concept of how incremental learning can make taxonomic classification much more efficient in its training process, significantly reducing computation while maintaining accuracy. In addition to predicting taxonomic labels for metagenomic samples, researchers are also interested in identifying different subtypes for one virus since mutations can be introduced during the transmission. "Oligotyping" is an entropy analysis tool developed for subtyping taxonomic units based on 16S rRNA sequences. "Oligotyping" was formulated because the 16S rRNA gene is very conservative and there are only very few mutations in the 16S rRNA gene for some lineages. The SARS-CoV-2 genome, being months old, also has a relatively small amount of mutations. Therefore, the entropy analysis developed for 16S rRNA sequences can be adapted for SARS-CoV-2 viral genome subtyping. However, other researchers were only looking at sequence similarity (and subsequent trees) or important single nucleotide variants individually between the genomes. To my knowledge, I am the first to draw on the "Oligotyping" concept to group mutations as a "barcode" of the viral genome and extend it to define subtypes for SARS-CoV-2 viral genomes. I further add error correction to account for ambiguities in the sequences and, optionally, apply further compression by identifying patterns of base entropy correlation. I demonstrate its application in spatiotemporal analyses of real world SARS-CoV-2 sequences responsible for the COVID-19 pandemic. My method is validated by comparing the subtypes defined to similar subtypes discovered in other literature. Third, microbial survey data is not used efficiently for phenotype prediction. For example, a precise Crohn's disease prediction model can help diagnostics given stool samples collected from subjects. To predict Crohn's disease (or another phenotype) from microbiome composition, researchers usually start by grouping sequences that look similar together into an Operation Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV) and subsequently learn samples by examining OTU occurrences in different phenotypes. However, only looking at sequence similarity ignores the sequential information contained in DNA sequences. Bioinformatics has been inspired by successes in deep learning applications in Natural Language Processing (NLP). Both convolutional neural network (CNN) and recurrent neural network (RNN) have been utilized to learn DNA sequential information for applications such as transcription factor binding site classification. In my work, I propose to adapt deep learning architectures (such as RNN and attention mechanism) that have been widely used in NLP to develop a "phenotype" classifier. This Read2Pheno classifier can predict "phenotype" based on 16S rRNA reads. I demonstrate how the sequential information learned by the proposed model can provide insights on informative regions in DNA sequences/reads while making accurate predictions. The model is validated by comparing its accuracy with other baseline methods such as a random forest model trained with various features (standard OTU/ASV table and k-mers). Forth, there have been different deep learning based functional annotation models proposed recently. However, these models can only output one class of function annotation predictions, such as Gene Ontology (GO). It is convenient to have a tool that can output function predictions for both function annotation databases. In this work, I first extend the proposed Read2Pheno model to a function prediction model, AttentionGO, and compare the performance with both alignment based and deep learning based models to show that the proposed model can achieve comparable performance with additional interpretability. Second, I explore the possibility of using the proposed AttentionGO classifier in a multi-task learning model to predict three branches of GO terms and KEGG Orthology terms simultaneously. The multi-task learning model is compared with single-task models trained with individual tasks to demonstrate performance improvement.