Biological Networks
Biological Networks

Author: Rudiyanto Gunawan

Publisher: MDPI

Published: 2019-01-10

Total Pages: 175

ISBN-13: 3038974331

DOWNLOAD EBOOK

This book is a printed edition of the Special Issue "Biological Networks" that was published in Processes

Bioinformatics Applications Based On Machine Learning
Bioinformatics Applications Based On Machine Learning

Author: Pablo Chamoso

Publisher: MDPI

Published: 2021-09-01

Total Pages: 206

ISBN-13: 3036507604

DOWNLOAD EBOOK

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Network Models for Data Science
Network Models for Data Science

Author: Alan Julian Izenman

Publisher: Cambridge University Press

Published: 2022-12-31

Total Pages: 501

ISBN-13: 1108835767

DOWNLOAD EBOOK

This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

Handbook of Machine Learning Applications for Genomics
Handbook of Machine Learning Applications for Genomics

Author: Sanjiban Sekhar Roy

Publisher: Springer Nature

Published: 2022-06-23

Total Pages: 222

ISBN-13: 9811691584

DOWNLOAD EBOOK

Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.

Dynamics of Mathematical Models in Biology
Dynamics of Mathematical Models in Biology

Author: Alessandra Rogato

Publisher: Springer

Published: 2016-11-03

Total Pages: 154

ISBN-13: 3319457233

DOWNLOAD EBOOK

This volume focuses on contributions from both the mathematics and life science community surrounding the concepts of time and dynamicity of nature, two significant elements which are often overlooked in modeling process to avoid exponential computations. The book is divided into three distinct parts: dynamics of genomes and genetic variation, dynamics of motifs, and dynamics of biological networks. Chapters included in dynamics of genomes and genetic variation analyze the molecular mechanisms and evolutionary processes that shape the structure and function of genomes and those that govern genome dynamics. The dynamics of motifs portion of the volume provides an overview of current methods for motif searching in DNA, RNA and proteins, a key process to discover emergent properties of cells, tissues, and organisms. The part devoted to the dynamics of biological networks covers networks aptly discusses networks in complex biological functions and activities that interpret processes in cells. Moreover, chapters in this section examine several mathematical models and algorithms available for integration, analysis, and characterization. Once life scientists began to produce experimental data at an unprecedented pace, it become clear that mathematical models were necessary to interpret data, to structure information with the aim to unveil biological mechanisms, discover results, and make predictions. The second annual “Bringing Maths to Life” workshop held in Naples, Italy October 2015, enabled a bi-directional flow of ideas from and international group of mathematicians and biologists. The venue allowed mathematicians to introduce novel algorithms, methods, and software that may be useful to model aspects of life science, and life scientists posed new challenges for mathematicians.

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Author: Hemachandran K

Publisher: CRC Press

Published: 2022-04-14

Total Pages: 147

ISBN-13: 1000569586

DOWNLOAD EBOOK

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

A Primer in Mathematical Models in Biology
A Primer in Mathematical Models in Biology

Author: Lee A. Segel

Publisher: SIAM

Published: 2013-05-09

Total Pages: 435

ISBN-13: 1611972493

DOWNLOAD EBOOK

A textbook on mathematical modelling techniques with powerful applications to biology, combining theoretical exposition with exercises and examples.

Biocomputing 2023 - Proceedings Of The Pacific Symposium
Biocomputing 2023 - Proceedings Of The Pacific Symposium

Author: Russ B Altman

Publisher: World Scientific

Published: 2022-11-24

Total Pages: 572

ISBN-13: 9811270627

DOWNLOAD EBOOK

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.