Deep Learning For Biological Network Analysis

DOWNLOAD

Download Deep Learning For Biological Network Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning For Biological Network Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Deep Learning For Biological Network Analysis

DOWNLOAD
Author : Jianye Hao
language : en
Publisher: Frontiers Media SA
Release Date : 2022-02-07

Deep Learning For Biological Network Analysis written by Jianye Hao 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 2022-02-07 with Science categories.

Deep Learning In Genetics And Genomics

DOWNLOAD
Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-11-28

Deep Learning In Genetics And Genomics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Science categories.

Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences. - Embraces the potential that deep learning holds for understanding genome biology - Encourages further advances in this area, extending to all aspects of genomics research - Provides Deep Learning algorithms in genetic and genomic research

Biological Network Analysis

DOWNLOAD
Author : Pietro Hiram Guzzi
language : en
Publisher: Elsevier
Release Date : 2020-05-11

Biological Network Analysis written by Pietro Hiram Guzzi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-11 with Science categories.

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. - Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models - Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes - Includes a discussion of various graph theoretic and data analytics approaches

Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods

DOWNLOAD
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.

Artificial Intelligence In Bioinformatics

DOWNLOAD
Author : Mario Cannataro
language : en
Publisher: Elsevier
Release Date : 2022-05-18

Artificial Intelligence In Bioinformatics written by Mario Cannataro and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-18 with Computers categories.

Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences Brings readers up-to-speed on current trends and methods in a dynamic and growing field Provides academic teachers with a complete resource, covering fundamental concepts as well as applications

Deep Learning In Biology And Medicine

DOWNLOAD
Author : Davide Bacciu
language : en
Publisher: World Scientific
Release Date : 2022-01-17

Deep Learning In Biology And Medicine written by Davide Bacciu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Computers categories.

Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

Deep Learning For The Life Sciences

DOWNLOAD
Author : Bharath Ramsundar
language : en
Publisher: O'Reilly Media
Release Date : 2019-04-10

Deep Learning For The Life Sciences written by Bharath Ramsundar and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Science categories.

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Data Analytics In Bioinformatics

DOWNLOAD
Author : Rabinarayan Satpathy
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-20

Data Analytics In Bioinformatics written by Rabinarayan Satpathy 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 2021-01-20 with Computers categories.

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Biological Network Analysis

DOWNLOAD
Author : Pietro Hiram Guzzi
language : en
Publisher: Academic Press
Release Date : 2020-05-12

Biological Network Analysis written by Pietro Hiram Guzzi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-12 with Science categories.

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource.

Supervised Sequence Labelling With Recurrent Neural Networks

DOWNLOAD
Author : Alex Graves
language : en
Publisher: Springer
Release Date : 2012-02-06

Supervised Sequence Labelling With Recurrent Neural Networks written by Alex Graves and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-06 with Computers categories.

Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.