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

Author : Lilhore, Umesh Kumar,Kumar, Abhishek,Simaiya, Sarita,Vyas, Narayan,Dutt, Vishal
Publisher : IGI Global
Page : 418 pages
File Size : 43,5 Mb
Release : 2024-03-22
Category : Computers
ISBN : 9798369318232

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

Author : Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi
Publisher : CRC Press
Page : 220 pages
File Size : 42,8 Mb
Release : 2021-10-17
Category : Technology & Engineering
ISBN : 9781000463019

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Advanced AI Techniques and Applications in Bioinformatics by Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi Pdf

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

Author : Musaddiq, Sara,Fayyaz, Imama,Mustafa, Kiran
Publisher : IGI Global
Page : 406 pages
File Size : 46,9 Mb
Release : 2024-05-07
Category : Medical
ISBN : 9798369361078

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Ethnobotanical Insights Into Medicinal Plants by Musaddiq, Sara,Fayyaz, Imama,Mustafa, Kiran Pdf

A significant gap exists between traditional knowledge and modern scientific understanding of phytochemicals and ethnobotanical wisdom in botanical science. Despite the commonplace culinary use of many herbs and seasonings, their historical, botanical, and medicinal dimensions often remain overlooked. This gap hinders advancements in various disciplines, including chemistry, pharmacology, botany, and agriculture, limiting the potential for innovative research and sustainable solutions. Ethnobotanical Insights into Medicinal Plants bridges this gap by comprehensively examining these plants' morphology, cultivation techniques, and classifications. This book illuminates their untapped potential and catalyzes innovative healthcare, agriculture, and manufacturing research. Integrating ethnobotanical observations with scientific progress enhances the intellectual domain for academics, researchers, and professionals, paving the way for environmentally sustainable methods of producing bioactive substances.

Author : Khan, Rijwan,Kumar, Indrajeet,Praveen, Pushkar
Publisher : IGI Global
Page : 367 pages
File Size : 49,5 Mb
Release : 2024-04-26
Category : Medical
ISBN : 9798369324271

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Applications of Parallel Data Processing for Biomedical Imaging by Khan, Rijwan,Kumar, Indrajeet,Praveen, Pushkar Pdf

Despite the remarkable progress witnessed in the last decade in big data utilization and parallel processing techniques, a persistent disparity exists between the capabilities of computer-aided diagnosis systems and the intricacies of practical healthcare scenarios. This disconnection is particularly evident in the complex landscape of artificial intelligence (AI) and IoT innovations within the biomedical realm. The need to bridge this gap and explore the untapped potential in healthcare and biomedical applications has never been more crucial. As we navigate through these challenges, Applications of Parallel Data Processing for Biomedical Imaging offers insights and solutions to reshape the future of biomedical research. The objective of Applications of Parallel Data Processing for Biomedical Imaging is to bring together researchers from both the computer science and biomedical research communities. By showcasing state-of-the-art deep learning and large data analysis technologies, the book provides a platform for the cross-pollination of ideas between AI-based and traditional methodologies. The collaborative effort seeks to have a substantial impact on data mining, AI, computer vision, biomedical research, healthcare engineering, and other related fields. This interdisciplinary approach positions the book as a cornerstone for scholars, professors, and professionals working in software and medical fields, catering to both graduate and undergraduate students eager to explore the evolving landscape of parallel computing, artificial intelligence, and their applications in biomedical research.

Author : Prabhakar, Pranav Kumar
Publisher : IGI Global
Page : 504 pages
File Size : 55,6 Mb
Release : 2024-05-06
Category : Technology & Engineering
ISBN : 9798369344408

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Reshaping Healthcare with Cutting-Edge Biomedical Advancements by Prabhakar, Pranav Kumar Pdf

Despite remarkable advancements in biomedical research, the healthcare industry faces challenges in effectively translating these discoveries into tangible patient benefits. Healthcare professionals often need help to keep pace with the rapid evolution of medical knowledge, leading to variations in patient care and treatment outcomes. Policymakers and educators may need more insight to leverage recent biomedical developments in shaping effective health policies and educational curricula. Additionally, ethical considerations surrounding emerging technologies like gene editing and Artificial Intelligence (AI) in healthcare pose complex dilemmas that require careful navigation. Reshaping Healthcare with Cutting-Edge Biomedical Advancements offers a comprehensive solution to these challenges. By providing a detailed exploration of the latest breakthroughs in genomics, regenerative therapies, neurobiology, AI, and more, this book equips healthcare professionals with the knowledge needed to make informed decisions about patient care. It also guides policymakers and educators, offering insights into the implications of recent biomedical advancements for shaping health policies and educational programs.

Author : Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Publisher : CRC Press
Page : 384 pages
File Size : 50,6 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.

Author : Zheng Rong Yang
Publisher : World Scientific
Page : 337 pages
File Size : 40,6 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.

Author : Pablo Chamoso,Sara Rodríguez González,Mohd Saberi Mohamad,Alfonso González-Briones
Publisher : MDPI
Page : 206 pages
File Size : 52,8 Mb
Release : 2021-09-01
Category : Technology & Engineering
ISBN : 9783036507606

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Bioinformatics Applications Based On Machine Learning by Pablo Chamoso,Sara Rodríguez González,Mohd Saberi Mohamad,Alfonso González-Briones Pdf

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.

Author : Faheem Masoodi,Mohammad Quasim,Syed Bukhari,Sarvottam Dixit,Shadab Alam
Publisher : CRC Press
Page : 233 pages
File Size : 43,6 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

Author : Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publisher : John Wiley & Sons
Page : 544 pages
File Size : 45,9 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.

Author : Abedalrhman Alkhateeb,Luis Rueda
Publisher : Springer Nature
Page : 171 pages
File Size : 42,9 Mb
Release : 2023-12-15
Category : Science
ISBN : 9783031365027

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Machine Learning Methods for Multi-Omics Data Integration by Abedalrhman Alkhateeb,Luis Rueda Pdf

The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 42,7 Mb
Release : 2024-06-15
Category : Electronic
ISBN : 9789814466783

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Machine Learning Approaches to Bioinformatics by Anonim Pdf

Author : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
Publisher : Springer Nature
Page : 318 pages
File Size : 47,6 Mb
Release : 2020-01-30
Category : Technology & Engineering
ISBN : 9789811524455

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications by K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar Pdf

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Author : Quan Zou,Arun Kumar Sangaiah,Dariusz Mrozek
Publisher : Frontiers Media SA
Page : 485 pages
File Size : 46,6 Mb
Release : 2019-12-04
Category : Electronic
ISBN : 9782889632145

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Machine Learning Techniques on Gene Function Prediction by Quan Zou,Arun Kumar Sangaiah,Dariusz Mrozek Pdf

Author : Shyamasree Ghosh,Rathi Dasgupta
Publisher : Springer Nature
Page : 337 pages
File Size : 44,8 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.