Machine Learning In Biological Sciences

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Machine Learning in Biological Sciences

Shyamasree Ghosh,Rathi Dasgupta
Machine Learning in Biological Sciences Book PDF

Author : Shyamasree Ghosh,Rathi Dasgupta
Publisher : Springer Nature
Page : 337 pages
File Size : 49,6 Mb
Release : 2022-05-04
Category : Medical
ISBN : 9789811688812

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Machine Learning in Biological Sciences by Shyamasree Ghosh,Rathi Dasgupta Pdf

This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.

Deep Learning for the Life Sciences

Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
Deep Learning for the Life Sciences Book PDF

Author : Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
Publisher : O'Reilly Media
Page : 236 pages
File Size : 49,9 Mb
Release : 2019-04-10
Category : Science
ISBN : 9781492039808

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Deep Learning for the Life Sciences by Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande Pdf

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

Machine Learning and IoT

Shampa Sen,Leonid Datta,Sayak Mitra
Machine Learning and IoT Book PDF

Author : Shampa Sen,Leonid Datta,Sayak Mitra
Publisher : CRC Press
Page : 354 pages
File Size : 47,9 Mb
Release : 2018-07-04
Category : Computers
ISBN : 9781351029933

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Machine Learning and IoT by Shampa Sen,Leonid Datta,Sayak Mitra Pdf

This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.

Applications of Machine Learning and Deep Learning on Biological Data

Faheem Masoodi,Mohammad Quasim,Syed Bukhari,Sarvottam Dixit,Shadab Alam
Applications of Machine Learning and Deep Learning on Biological Data Book PDF

Author : Faheem Masoodi,Mohammad Quasim,Syed Bukhari,Sarvottam Dixit,Shadab Alam
Publisher : CRC Press
Page : 233 pages
File Size : 51,7 Mb
Release : 2023-03-13
Category : Computers
ISBN : 9781000833799

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Applications of Machine Learning and Deep Learning on Biological Data by Faheem Masoodi,Mohammad Quasim,Syed Bukhari,Sarvottam Dixit,Shadab Alam Pdf

The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms. Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment. Highlights include: Artificial Intelligence in treating and diagnosing schizophrenia An analysis of ML’s and DL’s financial effect on healthcare An XGBoost-based classification method for breast cancer classification Using ML to predict squamous diseases ML and DL applications in genomics and proteomics Applying ML and DL to biological data

Deep Learning In Biology And Medicine

Davide Bacciu,Paulo J G Lisboa,Alfredo Vellido
Deep Learning In Biology And Medicine Book PDF

Author : Davide Bacciu,Paulo J G Lisboa,Alfredo Vellido
Publisher : World Scientific
Page : 333 pages
File Size : 44,7 Mb
Release : 2022-01-17
Category : Computers
ISBN : 9781800610958

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Deep Learning In Biology And Medicine by Davide Bacciu,Paulo J G Lisboa,Alfredo Vellido Pdf

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.

Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods

Vishal Dutt,Abhishek Kumar,Umesh Kumar Lilhore,Sarita Simaiya,Narayan Vyas
Applying Machine Learning Techniques to Bioinformatics Few Shot and Zero Shot Methods Book PDF

Author : Vishal Dutt,Abhishek Kumar,Umesh Kumar Lilhore,Sarita Simaiya,Narayan Vyas
Publisher : Unknown
Page : 0 pages
File Size : 46,8 Mb
Release : 2024
Category : Electronic
ISBN : 9798369318256

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Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods by Vishal Dutt,Abhishek Kumar,Umesh Kumar Lilhore,Sarita Simaiya,Narayan Vyas Pdf

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. Designed for academic scholars and practitioners, as well as upper-level undergraduates and graduates seeking to expand their knowledge, this book is a must-read for anyone passionate about the intersection of data science and human biology. Healthcare professionals, biotechnologists, and academics alike will find this resource invaluable for advancing their understanding and capabilities in the dynamic field of bioinformatics.

Computational and Analytic Methods in Biological Sciences

Akshara Makrariya,Brajesh Kumar Jha,Rabia Musheer,Anant Kant Shukla,Amrita Jha,Parvaiz Ahmad Naik
Computational and Analytic Methods in Biological Sciences Book PDF

Author : Akshara Makrariya,Brajesh Kumar Jha,Rabia Musheer,Anant Kant Shukla,Amrita Jha,Parvaiz Ahmad Naik
Publisher : CRC Press
Page : 325 pages
File Size : 47,7 Mb
Release : 2023-05-31
Category : Computers
ISBN : 9781000879872

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Computational and Analytic Methods in Biological Sciences by Akshara Makrariya,Brajesh Kumar Jha,Rabia Musheer,Anant Kant Shukla,Amrita Jha,Parvaiz Ahmad Naik Pdf

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.

Data Analytics in Bioinformatics

Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Data Analytics in Bioinformatics Book PDF

Author : Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 55,6 Mb
Release : 2021-01-20
Category : Computers
ISBN : 9781119785613

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Data Analytics in Bioinformatics by Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang Pdf

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.

Introduction to Machine Learning and Bioinformatics

Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Introduction to Machine Learning and Bioinformatics Book PDF

Author : Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Publisher : CRC Press
Page : 384 pages
File Size : 48,6 Mb
Release : 2008-06-05
Category : Mathematics
ISBN : 9781420011784

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Introduction to Machine Learning and Bioinformatics by Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis Pdf

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

A Biologist’s Guide to Artificial Intelligence

Ambreen Hamadani,Nazir A Ganai,Hamadani Henna,J Bashir
A Biologist s Guide to Artificial Intelligence Book PDF

Author : Ambreen Hamadani,Nazir A Ganai,Hamadani Henna,J Bashir
Publisher : Elsevier
Page : 370 pages
File Size : 42,6 Mb
Release : 2024-03-15
Category : Computers
ISBN : 9780443240003

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A Biologist’s Guide to Artificial Intelligence by Ambreen Hamadani,Nazir A Ganai,Hamadani Henna,J Bashir Pdf

A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence

A Guide to Applied Machine Learning for Biologists

Mohammad "Sufian" Badar
A Guide to Applied Machine Learning for Biologists Book PDF

Author : Mohammad "Sufian" Badar
Publisher : Unknown
Page : 0 pages
File Size : 49,7 Mb
Release : 2023
Category : Electronic
ISBN : 3031222083

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A Guide to Applied Machine Learning for Biologists by Mohammad "Sufian" Badar Pdf

This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.

Machine Learning and Systems Biology in Genomics and Health

Shailza Singh
Machine Learning and Systems Biology in Genomics and Health Book PDF

Author : Shailza Singh
Publisher : Springer Nature
Page : 239 pages
File Size : 44,5 Mb
Release : 2022-02-04
Category : Science
ISBN : 9789811659935

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Machine Learning and Systems Biology in Genomics and Health by Shailza Singh Pdf

This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

Machine Learning Approaches to Bioinformatics

Zheng Rong Yang
Machine Learning Approaches to Bioinformatics Book PDF

Author : Zheng Rong Yang
Publisher : World Scientific
Page : 337 pages
File Size : 40,7 Mb
Release : 2010
Category : Computers
ISBN : 9789814287319

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Machine Learning Approaches to Bioinformatics by Zheng Rong Yang Pdf

This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.

Deep Learning for the Life Sciences

Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
Deep Learning for the Life Sciences Book PDF

Author : Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
Publisher : "O'Reilly Media, Inc."
Page : 244 pages
File Size : 48,8 Mb
Release : 2019-04-10
Category : Science
ISBN : 9781492039785

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Deep Learning for the Life Sciences by Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande Pdf

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

Gene Expression Data Analysis

Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Gene Expression Data Analysis Book PDF

Author : Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 379 pages
File Size : 43,5 Mb
Release : 2021-11-21
Category : Computers
ISBN : 9781000425734

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Gene Expression Data Analysis by Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences