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Explainable and Transparent AI and Multi-Agent Systems
This book constitutes the refereed proceedings of the 4th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2022, held virtually during May 9–10, 2022. The 14 full papers included in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: explainable machine learning; explainable neuro-symbolic AI; explainable agents; XAI measures and metrics; and AI & law.
Explainable Artificial Intelligence in Medical Imaging
Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered. Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-s...
Systems Analytics and Integration of Big Omics Data
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
Bioinformatics Research and Applications
This book constitutes the proceedings of the 14th International Conference on Bioinformatics Research and Applications, ISBRA 2018, held in Beijing, China, in June 2018. The 24 full and 10 short papers presented in this volume were carefully reviewed and selected from a total of 138 submissions. They were organized in topical sections named: network analysis and modelling; genomic data analysis; cancer data analysis; structure and interaction; HPC and CryoEM; machine and deep learning; data analysis and methodology; analysis and visualization tools; and RNA-Seq data analysis.
Research in Computational Molecular Biology
This book constitutes the proceedings of the 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020, held in Padua, Italy, in May 2020. The 13 regular and 24 short papers presented were carefully reviewed and selected from 206 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.
Deep Learning in Drug Design
Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, applications, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models for drugs, and the deep learning applications in various aspects of drug design. This book will give readers an intuitive and simple understanding of the encoding and decoding of drugs for model training, while deep learning methods profile the different training perspectives for drug design including sequence-based, 2D, and 3D drug design based on geometric deep learning. This book is suitable for reader...
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