Machine Learning For The Prediction Of Protein Protein Interactions
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Machine Learning For The Prediction Of Protein Protein Interactions
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Author : Jos? Antonio Reyes
language : en
Publisher:
Release Date : 2010
Machine Learning For The Prediction Of Protein Protein Interactions written by Jos? Antonio Reyes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
The prediction of protein-protein interactions (PPI) has recently emerged as an important problem in the fields of bioinformatics and systems biology, due to the fact that most essential cellular processes are mediated by these kinds of interactions. In this thesis we focussed in the prediction of co-complex interactions, where the objective is to identify and characterize protein pairs which are members of the same protein complex. Although high-throughput methods for the direct identification of PPI have been developed in the last years. It has been demonstrated that the data obtained by these methods is often incomplete and suffers from high false-positive and false-negative rates. In order to deal with this technology-driven problem, several machine learning techniques have been employed in the past to improve the accuracy and trustability of predicted protein interacting pairs, demonstrating that the combined use of direct and indirect biological insights can improve the quality of predictive PPI models. This task has been commonly viewed as a binary classification problem. However, the nature of the data creates two major problems. Firstly, the imbalanced class problem due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly, the selection of negative examples is based on some unreliable assumptions which could introduce some bias in the classification results. The first part of this dissertation addresses these drawbacks by exploring the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilize examples of just one class to generate a predictive model which is consequently independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We designed and carried out a performance evaluation study of several OCC methods for this task. We also undertook a comparative performance evaluation with several conventional learning techniques. Furthermore, we pay attention to a new potential drawback which appears to affect the performance of PPI prediction. This is associated with the composition of the positive gold standard set, which contain a high proportion of examples associated with interactions of ribosomal proteins. We demonstrate that this situation indeed biases the classification task, resulting in an over-optimistic performance result. The prediction of non-ribosomal PPI is a much more difficult task. We investigate some strategies in order to improve the performance of this subtask, integrating new kinds of data as well as combining diverse classification models generated from different sets of data. In this thesis, we undertook a preliminary validation study of the new PPI predicted by using OCC methods. To achieve this, we focus in three main aspects: look for biological evidence in the literature that support the new predictions; the analysis of predicted PPI networks properties; and the identification of highly interconnected groups of proteins which can be associated with new protein complexes. Finally, this thesis explores a slightly different area, related to the prediction of PPI types. This is associated with the classification of PPI structures (complexes) contained in the Protein Data Bank (PDB) data base according to its function and binding affinity. Considering the relatively reduced number of crystalized protein complexes available, it is not possible at the moment to link these results with the ones obtained previously for the prediction of PPI complexes. However, this could be possible in the near future when more PPI structures will be available.
Machine Learning And Optimization Methods For The Prediction Of Protein Protein Interactions
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Author : Mudassar Iqbal
language : en
Publisher:
Release Date : 2009
Machine Learning And Optimization Methods For The Prediction Of Protein Protein Interactions written by Mudassar Iqbal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Exploring Feature Identification And Machine Learning In Predicting Protein Protein Interactions Of Disordered Proteins
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Author : Gözde Kibar
language : en
Publisher:
Release Date : 2024
Exploring Feature Identification And Machine Learning In Predicting Protein Protein Interactions Of Disordered Proteins written by Gözde Kibar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Protein Protein Interactions
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Author : Shahid Mukhtar
language : en
Publisher: Springer Nature
Release Date : 2023-07-14
Protein Protein Interactions written by Shahid Mukhtar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-14 with Science categories.
This detailed volume provides a comprehensive collection of classic and cutting-edge methods and techniques in mapping protein-protein interactions. The chapters include a variety of in vitro and in vivo experimental methods covering cell biology, biochemistry, and biophysics. In addition, the book also explores in silico methods including sequence-, structure-, and phylogenetic profile-based approaches as well as gene expression and machine learning methods. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step and readily reproducible laboratory protocols, as well as tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Protein-Protein Interactions: Methods and Protocols serves as an ideal guide for researchers working in protein science and beyond.
Proteome Wide Prediction Of Protein Protein Interactions Based On Coevolution And Deep Learning
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Author : Tao Fang
language : en
Publisher:
Release Date : 2023
Proteome Wide Prediction Of Protein Protein Interactions Based On Coevolution And Deep Learning written by Tao Fang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Improving Protein Interactions Prediction Using Machine Learning And Visual Analytics
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Author : Mudita Singhal
language : en
Publisher:
Release Date : 2007
Improving Protein Interactions Prediction Using Machine Learning And Visual Analytics written by Mudita Singhal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Machine learning categories.
Computational Intelligence In Protein Ligand Interaction Analysis
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Author : Bing Wang
language : en
Publisher: Academic Press
Release Date : 2024-03-22
Computational Intelligence In Protein Ligand Interaction Analysis written by Bing Wang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Science categories.
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data and computational intelligence methods for protein-ligand interactions. Presents a guide to computational techniques for protein-ligand interaction analysis Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem Identifies appropriate computational tools for various problems Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning Offers the computational, mathematical and statistical skills researchers need
Proteomics Data Analysis
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Author : Daniela Cecconi
language : en
Publisher:
Release Date : 2021
Proteomics Data Analysis written by Daniela Cecconi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Proteomics categories.
This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.
Introduction To Protein Structure Prediction
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Author : Huzefa Rangwala
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-16
Introduction To Protein Structure Prediction written by Huzefa Rangwala and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-16 with Science categories.
A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Protein Interactions Computational Methods Analysis And Applications
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Author : M Michael Gromiha
language : en
Publisher: World Scientific
Release Date : 2020-03-05
Protein Interactions Computational Methods Analysis And Applications written by M Michael Gromiha and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-05 with Science categories.
This book is indexed in Chemical Abstracts ServiceThe interactions of proteins with other molecules are important in many cellular activities. Investigations have been carried out to understand the recognition mechanism, identify the binding sites, analyze the the binding affinity of complexes, and study the influence of mutations on diseases. Protein interactions are also crucial in structure-based drug design.This book covers computational analysis of protein-protein, protein-nucleic acid and protein-ligand interactions and their applications. It provides up-to-date information and the latest developments from experts in the field, using illustrations to explain the key concepts and applications. This volume can serve as a single source on comparative studies of proteins interacting with proteins/DNAs/RNAs/carbohydrates and small molecules.