Scalable Computational Methods For The Analysis Of High Throughput Biological Data

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Scalable Computational Methods for the Analysis of High-Throughput Biological Data

Author :
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (953 download)

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Book Synopsis Scalable Computational Methods for the Analysis of High-Throughput Biological Data by :

Download or read book Scalable Computational Methods for the Analysis of High-Throughput Biological Data written by and published by . This book was released on 2012 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: This primary focus of this research project is elucidating genetic regulatory mechanisms that control an organism's responses to low-dose ionizing radiation. Although low doses (at most ten centigrays) are not lethal to humans, they elicit a highly complex physiological response, with the ultimate outcome in terms of risk to human health unknown. The tools of molecular biology and computational science will be harnessed to study coordinated changes in gene expression that orchestrate the mechanisms a cell uses to manage the radiation stimulus. High performance implementations of novel algorithms that exploit the principles of fixed-parameter tractability will be used to extract gene sets suggestive of co-regulation. Genomic mining will be performed to scrutinize, winnow and highlight the most promising gene sets for more detailed investigation. The overall goal is to increase our understanding of the health risks associated with exposures to low levels of radiation.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher : MDPI
ISBN 13 : 3039437712
Total Pages : 222 pages
Book Rating : 4.0/5 (394 download)

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Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Technology and Method Developments for High-throughput Translational Medicine

Author : Junhee Seok
Publisher : Stanford University
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Technology and Method Developments for High-throughput Translational Medicine by : Junhee Seok

Download or read book Technology and Method Developments for High-throughput Translational Medicine written by Junhee Seok and published by Stanford University. This book was released on 2011 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Translation of knowledge from basic science to medicine is essential to improving both clinical research and practice. In this translation, high-throughput genomic approaches can greatly accelerate our understanding of molecular mechanisms of diseases. A successful high-throughput genomic study of disease requires, first, comprehensive and efficient platforms to collect genomic data from clinical samples, and second, computational analysis methods that utilize databases of prior biological knowledge together with experimental data to derive clinically meaningful results. In this thesis, we discuss the development of a new microarray platform as well as computational methods for knowledge-based analysis along with their applications in clinical research. First, we and other colleagues have developed a new high-density oligonucleo-tide array of the human transcriptome for high-throughput and cost-efficient analysis of patient samples in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing, and also pro-vides assays for coding SNP detection and non-coding transcripts. Compared with high-throughput mRNA sequencing technology, we show that this array is highly re-producible in estimating gene and exon expression, and sensitive in detecting expres-sion changes. In addition, the exon-exon junction feature of this array is shown to im-prove detection efficiency for mRNA alternative splicing when combined with an ap-propriate computational method. We implemented the use of this array in a multi-center clinical program and have obtained comparable levels of high quality and re-producible data. With low costs and high throughputs for sample processing, we antic-ipate that this array platform will have a wide range of applications in high-throughput clinical studies. Second, we investigated knowledge-based methods that utilize prior know-ledge from biology and medicine to improve analysis and interpretation of high-throughput genomic data. We have developed knowledge-based methods to enrich our prior knowledge, illustrate dynamic response to external stimulus, and identify distur-bances in cellular pathways by chemical exposure, as well as discover hidden biological signatures for the prediction of patient outcomes. Finally, we applied a knowledge-based approach in a large scale genomic study of trauma patients. Cooperating with clinical information, prior knowledge improved the interpretation of common and dif-ferential genomic response to injury, and provided efficient risk assessment for patient outcomes. The clinical and genomic data as well as analysis results in this trauma study were systematically organized and provided to research communities as new knowledge of traumatic injury. The microarray platform and knowledge-based methods presented in this thesis provide appropriate research tools for high-throughput translational medicine in a large clinical setting. This thesis is expected to advance understanding and treatment for dis-eases, and finally, improve public health.

Scalable Optimization Algorithms for High-throughput Genomic Data

Author :
Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.:/5 (913 download)

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Book Synopsis Scalable Optimization Algorithms for High-throughput Genomic Data by :

Download or read book Scalable Optimization Algorithms for High-throughput Genomic Data written by and published by . This book was released on 2015 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Scalable Pattern Recognition Algorithms

Author : Pradipta Maji
Publisher : Springer Science & Business Media
ISBN 13 : 3319056301
Total Pages : 316 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Scalable Pattern Recognition Algorithms by : Pradipta Maji

Download or read book Scalable Pattern Recognition Algorithms written by Pradipta Maji and published by Springer Science & Business Media. This book was released on 2014-03-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

High Performance Computational Methods for Biological Sequence Analysis

Author : Tieng K. Yap
Publisher : Springer Science & Business Media
ISBN 13 : 1461313910
Total Pages : 219 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis High Performance Computational Methods for Biological Sequence Analysis by : Tieng K. Yap

Download or read book High Performance Computational Methods for Biological Sequence Analysis written by Tieng K. Yap and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field. This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes. High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.

Gene Expression Data Analysis

Author : Pankaj Barah
Publisher : CRC Press
ISBN 13 : 1000425738
Total Pages : 379 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Gene Expression Data Analysis by : Pankaj Barah

Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-21 with total page 379 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 biological sciences

Big Data Analytics in Genomics

Author : Ka-Chun Wong
Publisher : Springer
ISBN 13 : 3319412795
Total Pages : 426 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Big Data Analytics in Genomics by : Ka-Chun Wong

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.

Integrating Diverse Biological Sources and Computational Methods for the Analysis of High-throughput Expression Data

Author : Nitesh Kumar Singh
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (99 download)

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Book Synopsis Integrating Diverse Biological Sources and Computational Methods for the Analysis of High-throughput Expression Data by : Nitesh Kumar Singh

Download or read book Integrating Diverse Biological Sources and Computational Methods for the Analysis of High-throughput Expression Data written by Nitesh Kumar Singh and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher :
ISBN 13 : 9783039437726
Total Pages : 222 pages
Book Rating : 4.4/5 (377 download)

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Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 in Systems Biology

Author : Eugenio Cinquemani
Publisher : Springer Nature
ISBN 13 : 303085633X
Total Pages : 292 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Computational Methods in Systems Biology by : Eugenio Cinquemani

Download or read book Computational Methods in Systems Biology written by Eugenio Cinquemani and published by Springer Nature. This book was released on 2021-09-13 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Computational Methods in Systems Biology, CMSB 2021, held in Bordeaux, France, September 22–24, 2021.*The 13 full papers and 5 tool papers were carefully reviewed and selected from 32 submissions. The topics of interest include biological process modelling; biological system model verification, validation, analysis, and simulation; high-performance computational systems biology; model inference from experimental data; multi-scale modeling and analysis methods; computational approaches for synthetic biology; machine learning and data-driven approaches; microbial ecology modelling and analysis; methods and protocols for populations and their variability; models, applications, and case studies in systems and synthetic biology. The chapters "Microbial Community Decision Making Models in Batch", "Population design for synthetic gene circuits", "BioFVM-X: An MPI+OpenMP 3-D Simulator for Biological Systems" are published open access under a CC BY license (Creative Commons Attribution 4.0 International License). * The conference was held in a hybrid mode due to the COVID-19 pandemic.

High-Performance Algorithms for Mass Spectrometry-Based Omics

Author : Fahad Saeed
Publisher : Springer Nature
ISBN 13 : 3031019601
Total Pages : 146 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis High-Performance Algorithms for Mass Spectrometry-Based Omics by : Fahad Saeed

Download or read book High-Performance Algorithms for Mass Spectrometry-Based Omics written by Fahad Saeed and published by Springer Nature. This book was released on 2022-09-02 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.

Computational Methods in Systems Biology

Author : Alessandro Abate
Publisher : Springer Nature
ISBN 13 : 303060327X
Total Pages : 387 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Computational Methods in Systems Biology by : Alessandro Abate

Download or read book Computational Methods in Systems Biology written by Alessandro Abate and published by Springer Nature. This book was released on 2020-10-01 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Computational Methods in Systems Biology, CMSB 2020, held in Konstanz, Germany, in September 2020.* The 17 full papers and 5 tool papers were carefully reviewed and selected from 30 submissions. In addition 3 abstracts of invited talks and 2 tutorials have been included in this volume. Topics of interest include formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, analysis, and simulation of biological systems; high-performance computational systems biology and parallel implementations; model inference from experimental data; model integration from biological databases; multi-scale modeling and analysis methods; computational approaches for synthetic biology; and case studies in systems and synthetic biology. * The conference was held virtually due to the COVID-19 pandemic.

Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks

Author : Archana Ramesh
Publisher :
ISBN 13 :
Total Pages : 120 pages
Book Rating : 4.:/5 (851 download)

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Book Synopsis Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks by : Archana Ramesh

Download or read book Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks written by Archana Ramesh and published by . This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops a framework of computational methods for the analysis of such data-driven hypotheses leveraging existing biological knowledge. Specifically, I show how biological knowledge can be mapped onto these hypotheses and subsequently augmented through novel hypotheses. Biological hypotheses are learnt in three levels of abstraction -- individual interactions, functional modules and relationships between pathways, corresponding to three complementary aspects of biological systems. The computational methods developed in this dissertation are applied to high throughput cancer data, resulting in novel hypotheses with potentially significant biological impact.

Advances in Statistical Bioinformatics

Author : Kim-Anh Do
Publisher : Cambridge University Press
ISBN 13 : 1107244919
Total Pages : 499 pages
Book Rating : 4.1/5 (72 download)

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Book Synopsis Advances in Statistical Bioinformatics by : Kim-Anh Do

Download or read book Advances in Statistical Bioinformatics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2013-06-10 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.

Encyclopedia of Bioinformatics and Computational Biology

Author :
Publisher : Elsevier
ISBN 13 : 0128114320
Total Pages : 3421 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Encyclopedia of Bioinformatics and Computational Biology by :

Download or read book Encyclopedia of Bioinformatics and Computational Biology written by and published by Elsevier. This book was released on 2018-08-21 with total page 3421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

Computational Methods in Cell Biology

Author : Anand R. Asthagiri
Publisher : Academic Press
ISBN 13 : 0123884039
Total Pages : 427 pages
Book Rating : 4.1/5 (238 download)

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Book Synopsis Computational Methods in Cell Biology by : Anand R. Asthagiri

Download or read book Computational Methods in Cell Biology written by Anand R. Asthagiri and published by Academic Press. This book was released on 2012-04-13 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational methods are playing an ever increasing role in cell biology. This volume of Methods in Cell Biology focuses on Computational Methods in Cell Biology and consists of two parts: (1) data extraction and analysis to distill models and mechanisms, and (2) developing and simulating models to make predictions and testable hypotheses. Focuses on computational methods in cell biology Split into 2 parts--data extraction and analysis to distill models and mechanisms, and developing and simulating models to make predictions and testable hypotheses Emphasizes the intimate and necessary connection with interpreting experimental data and proposing the next hypothesis and experiment