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Machine Learning Methodologies To Study Molecular Interactions
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Book Synopsis Machine Learning Methodologies To Study Molecular Interactions by : Elif Ozkirimli
Download or read book Machine Learning Methodologies To Study Molecular Interactions written by Elif Ozkirimli and published by Frontiers Media SA. This book was released on 2022-01-21 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. Elif Ozkirimli is a full time employee of F. Hoffmann-La Roche AG, Switzerland and Dr. Artur Yakimovich is a full time employee of Roche Products Limited, UK. All other Topic Editors declare no competing interests with regards to the Research Topic.
Book Synopsis Machine learning for biological sequence analysis by : Quan Zou
Download or read book Machine learning for biological sequence analysis written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-03-09 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by : Wojciech Samek
Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Book Synopsis Biomolecular Interactions Using Machine Learning by : Joel Robert Bock
Download or read book Biomolecular Interactions Using Machine Learning written by Joel Robert Bock and published by . This book was released on 2003 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques by : Lodhi, Huma
Download or read book Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques written by Lodhi, Huma and published by IGI Global. This book was released on 2010-07-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.
Book Synopsis Microbiome and Machine Learning by : Isabel Moreno Indias
Download or read book Microbiome and Machine Learning written by Isabel Moreno Indias and published by Frontiers Media SA. This book was released on 2022-08-02 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Computational Toxicology by : Huixiao Hong
Download or read book Advances in Computational Toxicology written by Huixiao Hong and published by Springer. This book was released on 2019-05-21 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making. The authors from various government agencies such as FDA, NCATS and NIEHS industry, and academic institutes share their real-world experience and discuss most current practices in computational toxicology and potential applications in regulatory science. Among the topics covered are molecular modeling and molecular dynamics simulations, machine learning methods for toxicity analysis, network-based approaches for the assessment of drug toxicity and toxicogenomic analyses. Offering a valuable reference guide to computational toxicology and potential applications in regulatory science, this book will appeal to chemists, toxicologists, drug discovery and development researchers as well as to regulatory scientists, government reviewers and graduate students interested in this field.
Book Synopsis Machine Learning Techniques on Gene Function Prediction by : Quan Zou
Download or read book Machine Learning Techniques on Gene Function Prediction written by Quan Zou and published by Frontiers Media SA. This book was released on 2019-12-04 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Protein-Nucleic Acid Interactions by : Phoebe A. Rice
Download or read book Protein-Nucleic Acid Interactions written by Phoebe A. Rice and published by Royal Society of Chemistry. This book was released on 2008-05-22 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides both in-depth background and up-to-date information in this area. The chapters are organized by general themes and principles, written by experts who illustrate topics with current findings. Topics covered include: - the role of ions and hydration in protein-nucleic acid interactions - transcription factors and combinatorial specificity - indirect readout of DNA sequence - single-stranded nucleic acid binding proteins - nucleic acid junctions and proteins, - RNA protein recognition - recognition of DNA damage. It will be a key reference for both advanced students and established scientists wishing to broaden their horizons.
Book Synopsis Machine Learning in Biological Sciences by : Shyamasree Ghosh
Download or read book Machine Learning in Biological Sciences written by Shyamasree Ghosh and published by Springer Nature. This book was released on 2022-05-04 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Molecular Computing and Bioinformatics by : Xiangxiang Zeng
Download or read book Molecular Computing and Bioinformatics written by Xiangxiang Zeng and published by MDPI. This book was released on 2019-07-11 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text will provide the most recent knowledge and advances in the area of molecular computing and bioinformatics. Molecular computing and bioinformatics have a close relationship, paying attention to the same object but working towards different orientations. The articles will range from topics such as DNA computing and membrane computing to specific biomedical applications, including drug R&D and disease analysis.
Book Synopsis Machine Learning in Molecular Sciences by : Chen Qu
Download or read book Machine Learning in Molecular Sciences written by Chen Qu and published by Springer Nature. This book was released on 2023-11-02 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
Book Synopsis Machine Learning Methods for Multi-Omics Data Integration by : Abedalrhman Alkhateeb
Download or read book Machine Learning Methods for Multi-Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis System Biology Methods and Tools for Integrating Omics Data by : Liang Cheng
Download or read book System Biology Methods and Tools for Integrating Omics Data written by Liang Cheng and published by Frontiers Media SA. This book was released on 2020-12-31 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Book Synopsis Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases by : Victor Chang
Download or read book Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases written by Victor Chang and published by Springer Nature. This book was released on 2022-06-28 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Springer book provides a perfect platform to submit chapters that discuss the prospective developments and innovative ideas in artificial intelligence and machine learning techniques in the diagnosis of COVID-19. COVID-19 is a huge challenge to humanity and the medical sciences. So far as of today, we have been unable to find a medical solution (Vaccine). However, globally, we are still managing the use of technology for our work, communications, analytics, and predictions with the use of advancement in data science, communication technologies (5G & Internet), and AI. Therefore, we might be able to continue and live safely with the use of research in advancements in data science, AI, machine learning, mobile apps, etc., until we can find a medical solution such as a vaccine. We have selected eleven chapters after the vigorous review process. Each chapter has demonstrated the research contributions and research novelty. Each group of authors must fulfill strict requirements.
Book Synopsis Intelligent Computing Theories and Application by : De-Shuang Huang
Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer. This book was released on 2019-09-22 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Book Synopsis Systems Biology Approaches for Host-Pathogen Interaction Analysis by : Mohd. Tashfeen Ashraf
Download or read book Systems Biology Approaches for Host-Pathogen Interaction Analysis written by Mohd. Tashfeen Ashraf and published by Elsevier. This book was released on 2024-02-16 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Biology Approaches for Microbial Pathogenesis Interaction Analysis aids biological researchers to expand their research scope using piled up data generated through recent technological advancement. In addition, it also opens avenues for bioinformatics and computer science researchers to utilize their expertise in biological meaningful ways. It also covers network biology approaches to decipher complex multiple host-pathogen interactions in addition to giving valuable coverage of artificial intelligence. The host-pathogen interactions are generally considered as highly specific interactions leading to a variety of consequences. The utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding consequences of these interaction in the biological network. Unfortunately, basic biological researchers are mostly unaware of these advancements. In contrast, data scientists are not familiar with biological aspects of such data. System Biology Approaches for Microbial Pathogenesis Interaction Analysis will bridge these gaps through a new paradigm of understanding consequences of interaction in biological networks. • Cover approaches to decipher complex multiple host–pathogen interactions• Gives biological researcher an insight into the utilization of technological advancements in the field of host–pathogen interaction analyses in their work• Provides a new paradigm of understanding the consequences of host–pathogen interaction in biological systems
