Analysis of Gene Expression Data for Gene Ontology Based Protein Function Prediction

Analysis of Gene Expression Data for Gene Ontology Based Protein Function Prediction
Author: Robert Daniel Macholan
Publisher:
Total Pages: 103
Release: 2011
Genre: Computational biology
ISBN:
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A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.

Protein Function Prediction for Omics Era

Protein Function Prediction for Omics Era
Author: Daisuke Kihara
Publisher: Springer Science & Business Media
Total Pages: 316
Release: 2011-04-19
Genre: Medical
ISBN: 9400708815
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Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Gene Expression Data Analysis

Gene Expression Data Analysis
Author: Pankaj Barah
Publisher: CRC Press
Total Pages: 379
Release: 2021-11-21
Genre: Computers
ISBN: 1000425738
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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

The Gene Ontology Handbook

The Gene Ontology Handbook
Author: Christophe Dessimoz
Publisher:
Total Pages: 298
Release: 2020-10-08
Genre: Science
ISBN: 9781013267710
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This book provides a practical and self-contained overview of the Gene Ontology (GO), the leading project to organize biological knowledge on genes and their products across genomic resources. Written for biologists and bioinformaticians, it covers the state-of-the-art of how GO annotations are made, how they are evaluated, and what sort of analyses can and cannot be done with the GO. In the spirit of the Methods in Molecular Biology book series, there is an emphasis throughout the chapters on providing practical guidance and troubleshooting advice. Authoritative and accessible, The Gene Ontology Handbook serves non-experts as well as seasoned GO users as a thorough guide to this powerful knowledge system. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Gene Function Analysis

Gene Function Analysis
Author: Michael F. Ochs
Publisher: Springer Science & Business Media
Total Pages: 703
Release: 2007-08-23
Genre: Medical
ISBN: 1588297349
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With the advent of high-throughput technologies following completion of the human genome project and similar projects, the number of genes of interest has expanded and the traditional methods for gene function analysis cannot achieve the throughput necessary for large-scale exploration. This book brings together a number of recently developed techniques for looking at gene function, including computational, biochemical and biological methods and protocols.

Knowledge-Based Bioinformatics

Knowledge-Based Bioinformatics
Author: Gil Alterovitz
Publisher: John Wiley & Sons
Total Pages: 306
Release: 2011-04-20
Genre: Medical
ISBN: 1119995833
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There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

A Gene Ontology Based Computational Approach for the Prediction of Protein Functions

A Gene Ontology Based Computational Approach for the Prediction of Protein Functions
Author: Saket Kharsikar
Publisher:
Total Pages: 92
Release: 2007
Genre: Biomedical engineering
ISBN:
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Numerous genome projects have produced a large and ever increasing amount of genomic sequence data. However, the biological functions of many proteins encoded by the sequences remain unknown. Protein function annotation and prediction become an essential and challenging task of post-genomic research. In this research, we present an automated protein function prediction system based on a set of proteins of known biological functions. The functions of the proteins are characterized with Gene Ontology (GO) annotations. The prediction system uses a novel measure to calculate the pair-wise overall similarity between protein sequences. The protein function prediction is performed based on the GO annotations of similar sequences using a weighted k-nearest neighbor method. We show the prediction accuracies obtained using the model organism yeast (Sacchyromyces cerevisiae). The results indicate that the weighted k-nearest neighbor method significantly outperforms the regular k-nearest neighbor method for protein biological function prediction.

Multiple Comparison Procedures

Multiple Comparison Procedures
Author: Yosef Hochberg
Publisher:
Total Pages: 482
Release: 1987-10-05
Genre: Mathematics
ISBN:
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Offering a balanced, up-to-date view of multiple comparison procedures, this book refutes the belief held by some statisticians that such procedures have no place in data analysis. With equal emphasis on theory and applications, it establishes the advantages of multiple comparison techniques in reducing error rates and in ensuring the validity of statistical inferences. Provides detailed descriptions of the derivation and implementation of a variety of procedures, paying particular attention to classical approaches and confidence estimation procedures. Also discusses the benefits and drawbacks of other methods. Numerous examples and tables for implementing procedures are included, making this work both practical and informative.

Big Data Analytics in Genomics

Big Data Analytics in Genomics
Author: Ka-Chun Wong
Publisher: Springer
Total Pages: 426
Release: 2016-10-24
Genre: Computers
ISBN: 3319412795
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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.

Gene Prediction: Applying Ontology and Machine Learning (Volume I)

Gene Prediction: Applying Ontology and Machine Learning (Volume I)
Author: Casper Harvey
Publisher: Larsen and Keller Education
Total Pages: 0
Release: 2023-09-26
Genre: Science
ISBN:
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Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.