Machine Learning Techniques On Gene Function Prediction
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Machine Learning Techniques On Gene Function Prediction
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2019-12-04
Machine Learning Techniques On Gene Function Prediction written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-04 with categories.
Machine Learning Techniques On Gene Function Prediction Volume Ii
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-11
Machine Learning Techniques On Gene Function Prediction Volume Ii written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-11 with Science categories.
Gene Function Prediction Applying Machine Learning Techniques
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Author : John Jackson
language : en
Publisher:
Release Date : 2025-08-25
Gene Function Prediction Applying Machine Learning Techniques written by John Jackson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Science categories.
A gene is a fundamental unit of heredity that is composed of a specific sequence of nucleotides within a DNA molecule. It contains the instructions for building and maintaining the structures and functions of living organisms. Gene function prediction involves inferring the biological function of a gene based on various types of data, such as sequence information, gene expression profiles, protein-protein interactions, and evolutionary conservation. Machine learning has emerged as a powerful tool for gene function prediction due to its ability to analyze large and complex datasets, identify patterns, and make predictions. Machine Learning algorithms, including supervised, unsupervised, and semi-supervised techniques, are applied to gene function prediction tasks. Some algorithms commonly used for gene function prediction include decision trees, random forests, support vector machines, neural networks, and deep learning architectures like convolutional neural networks and recurrent neural networks. ML-based gene function prediction has numerous applications, including prioritizing candidate genes for functional validation, identifying drug targets, understanding disease mechanisms, and predicting gene-disease associations. This book presents the complex subject of gene function prediction in the most comprehensible and easy to understand language. The objective of this book is to give a general view of the different areas of machine learning and its applications in gene function prediction. This book is a vital tool for all researching or studying gene function prediction as it gives incredible insights into emerging trends and concepts.
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-22
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.
Genomics At The Nexus Of Ai Computer Vision And Machine Learning
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Author : Shilpa Choudhary
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-01
Genomics At The Nexus Of Ai Computer Vision And Machine Learning written by Shilpa Choudhary 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 2024-10-01 with Computers categories.
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
Neural Information Processing
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Author : Jun Wang
language : en
Publisher: Springer
Release Date : 2006-10-03
Neural Information Processing written by Jun Wang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-03 with Computers categories.
The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions.
Neural Information Processing
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Author : Irwin King
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-22
Neural Information Processing written by Irwin King and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-22 with Computers categories.
The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions.
Mathematical Theory And Computational Practice
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Author : Klaus Ambos-Spies
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-15
Mathematical Theory And Computational Practice written by Klaus Ambos-Spies and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-15 with Computers categories.
This book constitutes the proceedings of the 5th Conference on Computability in Europe, CiE 2009, held in Heidelberg, Germany, during July 19-24, 2009. The 34 papers presented together with 17 invited lectures were carefully reviewed and selected from 100 submissions. The aims of the conference is to advance our theoretical understanding of what can and cannot be computed, by any means of computation. It is the largest international meeting focused on computability theoretic issues.
Biomolecular Networks
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Author : Luonan Chen
language : en
Publisher: John Wiley & Sons
Release Date : 2009-06-29
Biomolecular Networks written by Luonan Chen 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 2009-06-29 with Computers categories.
Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.
Protein Function Prediction For Omics Era
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Author : Daisuke Kihara
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-19
Protein Function Prediction For Omics Era written by Daisuke Kihara and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-19 with Medical categories.
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