Download or read book Protein Function Prediction for Omics Era written by Daisuke Kihara and published by Springer Science & Business Media. This book was released on 2011-04-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book New Approaches of Protein Function Prediction from Protein Interaction Networks written by Jingyu Hou and published by Academic Press. This book was released on 2017-01-13 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Approaches of Protein Function Prediction from Protein Interaction Networks contains the critical aspects of PPI network based protein function prediction, including semantically assessing the reliability of PPI data, measuring the functional similarity between proteins, dynamically selecting prediction domains, predicting functions, and establishing corresponding prediction frameworks. Functional annotation of proteins is vital to biological and clinical research and other applications due to the important roles proteins play in various biological processes. Although the functions of some proteins have been annotated via biological experiments, there are still many proteins whose functions are yet to be annotated due to the limitations of existing methods and the high cost of experiments. To overcome experimental limitations, this book helps users understand the computational approaches that have been rapidly developed for protein function prediction. Provides innovative approaches and new developments targeting key issues in protein function prediction Presents heuristic ideas for further research in this challenging area

Download or read book Protein Function Prediction from Protein Interaction Network written by Sovan Saha and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins perform every function in a cell. With the advent of genome sequencing projects for different organisms, large amounts of DNA and protein sequence data is available, whereas their biological function is still unknown in the most of the cases. Predicting protein function is the most challenging problem in post-genomic era. Using sequence homology, phylogenetic profiles, gene expression data, and function of unknown protein can be predicted. Recently, the large interaction networks constructed from high throughput techniques like Yeast2Hybrid experiments are also used in prediction of protein function. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Based on the concept that a protein performs similar function like its neighbor in protein interaction network, a method is proposed to predict protein function using protein-protein interaction data.This analysis should enlighten the path for predicting unannotated protein function hence identifying diseases and inventing methods of it's cureness.

Download or read book Protein Function Prediction Methods and Protocols written by Daisuke Kihara and published by Methods in Molecular Biology. This book was released on 2019-05-12 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analyzing Network Data in Biology and Medicine written by Nataša Pržulj and published by Cambridge University Press. This book was released on 2019-03-28 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.

Download or read book Biological Knowledge Discovery Handbook written by Mourad Elloumi and published by John Wiley & Sons. This book was released on 2015-02-04 with total page 1126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD) providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing also known as data mining and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

Download or read book Structure Based Drug Design for Diagnosis and Treatment of Neurological Diseases written by Rona R. Ramsay and published by Frontiers Media SA. This book was released on 2017-03-24 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: European Cooperation in Science and Technology (COST) supports the collaboration of nationally-funded science and technology research through the creation of networks. COST is the longest-running European framework enhancing cooperation among researchers, engineers and scholars across Europe. The COST Action CM1103 “Structure-based drug design for diagnosis and treatment of neurological diseases: dissecting and modulating complex function in the monoaminergic systems of the brain” is a good example of the advances possible through interdisciplinary collaboration on difficult problems. COST Action CM1103 brought together 28 research groups from 18 countries to collaborate for four years on multi-target drug design for complex neuropathologies. The interdisciplinary expertise of the members is spans the range from computational enzymology to human studies, providing outstanding opportunities for the interdisciplinary development of trainees, and is reflected in the articles in this e-book. This Research Topic covers progress in multi-target drug design for the complex neuropathologies of the monoamine system that are apparent, for example, in Alzheimer’s disease. After a mini-review to introduce the topic of multi-target drug design, the other articles review the Research topic from their own perspective, two from computational approaches, three from medicinal chemistry, two from molecular pharmacology, and two from studies in whole brain. This multi-faceted approach describes new compounds, new methodology, and advances in the basic science of understanding the brain. This Ebook is based upon work from COST Action (CM1103 “Structure-based drug design for diagnosis and treatment of neurological diseases: dissecting and modulating complex function in the monoaminergic systems of the brain"), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a pan-European intergovernmental framework. Its mission is to enable break-through scientific and technological developments leading to new concepts and products and thereby contribute to strengthening Europe’s research and innovation capacities. It allows researchers, engineers and scholars to jointly develop their own ideas and take new initiatives across all fields of science and technology, while promoting multi- and interdisciplinary approaches. COST aims at fostering a better integration of less research intensive countries to the knowledge hubs of the European Research Area. The COST Association, an International not-for-profit Association under Belgian Law, integrates all management, governing and administrative functions necessary for the operation of the framework. The COST Association has currently 36 Member Countries. www.cost.eu

Download or read book Protein Function Prediction written by Burkhard Rost and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book From Protein Structure to Function with Bioinformatics written by Daniel John Rigden and published by Springer. This book was released on 2010-11-10 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.

Download or read book Sequence based Protein Function Prediction written by Brett Poulin and published by . This book was released on 2004 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book PROTEIN FUNCTION PREDICTION BA written by Yatong An and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Protein Function Prediction Based on Pocket-specific Noncontiguous Amino Acid Subsequences" by Yatong, An, {273a67}亚{275c28}, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Building a protein functional repertoire is important for many life sciences. Unfortunately, less than 1% of protein sequences have been annotated with reliable evidence. The use of computational methods to predict protein functions has become a common means to bridge this formidable gap. In this thesis, it is proposed to use pocket-specific noncontiguous amino acid subsequences for predicting protein functions. These subsequence patterns have a strong function classification capability and are also complementary to protein sequence alignment methods. On the basis of a benchmark of ∼1600 testing proteins from the Protein Data Bank (PDB), It is demonstrated that function prediction using pocket-specific noncontiguous amino acid subsequences can be much more accurate than using three-dimensional pocket structures. Because these noncontiguous amino acid subsequences are independent of protein or pocket structures, the method based on such subsequence patterns can be easily applied to proteins with unknown structures. Predictors achieve state-of-the-art performance on two benchmarks constructed using proteins from the PDB and SwissProt respectively. Then protein sequence alignment features are further integrated into our pocket-specific noncontiguous subsequence model. The maximum F-measure of the integrated predictor on the PDB-based benchmark is 0.844 for the molecular function (MF) ontology and 0.838 for the biological process (BP) ontology, representing respective performance improvements of 47.8% and 48.3% over best results achieved with existing methods. On the SwissProt-based benchmark, the maximum Fmeasure of the integrated predictor is 0.627 for MF and 0.468 for BP, representing respective performance improvements of 29.0% and 38.1% over best results achieved with existing methods. Subjects: Amino acid sequence Proteomics - Data processing

Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Download or read book Computational Approaches to Predicting Protein Function from Structure written by Eric W. Stawiski and published by . This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Protein Function Prediction by Integrating Sequence Structure and Binding Affinity Information written by Huiying Zhao and published by . This book was released on 2013 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA, RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Download or read book A Gene Ontology Based Computational Approach for the Prediction of Protein Functions written by Saket Kharsikar and published by . This book was released on 2007 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Prediction of Protein Function and Functional Sites from Protein Sequences written by Jing Hu and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomics projects have resulted in a rapid accumulation of protein sequences. Therefore, computational methods that can predict protein functions and functional sites efficiently and accurately are in high demand. In addition, prediction methods utilizing only sequence information are of particular interest because for most proteins, 3-dimensional structures are not available. However, there are several key challenges in developing methods for predicting protein function and functional sites. These challenges include the following: the construction of representative datasets to train and evaluate the method, the collection of features related to the protein functions, the selection of the most useful features, and the integration of selected features into suitable computational models. In this proposed study, we tackle these challenges by developing procedures for benchmark dataset construction and protein feature extraction, implementing efficient feature selection strategies, and developing effective machine learning algorithms for protein function and functional site predictions. We investigate these challenges in three bioinformatics tasks: the discovery of transmembrane betabarrel (TMB) proteins in gram-negative bacterial proteomes, the identification of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs), and the identification of helix-turn-helix (HTH) motifs from protein sequence.

Download or read book Plant Derived Anticancer Drugs in the OMICS Era written by Deepu Pandita and published by CRC Press. This book was released on 2023-10-27 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current anti-cancer synthetic medicines are deemed inefficient and unsafe, state the editors of this new book. Plant-based lead molecules, however, such as taxol, camptothecin, podophyllotoxins, vinblastine, vincristine, homoharringtonine, and numerous other anticancer compounds from nature’s arsenal, are potentially safe and can be powerful alternatives that effectively fight against cancer. The volume looks at a variety of medicinal plants and approaches that have shown beneficial results against cancer. Topics in the book include Unani approaches of anticancer plants, genetic engineering and CRISPR/CAS-mediated editing to enhance a plant’s anticancer potential, computational approaches used in anticancer plants, and more. The volume also examines the metabolomics of plants that give them anti-cancer properties.