Single-cell OMICs analyses have recently become one of the most promising tools to probe biology at the cellular level, in large part due to its ability to address issues beyond the bulk analysis – a window into cellular heterogeneity. The ability to profile transcriptomic, epigenomic, proteomics, and metabolomics at the single cell level including more recently the spatial information has enhanced our ability to understand interactions between biomolecules in different contexts leading to the discovery of specific cellular subpopulations as well as biological mechanisms underlying pathologies which may be amenable to therapeutic interventions. The scale and availability of a variety of technologies to measure intricate molecular details have provided an impetus to research in many disease areas, including cardiovascular medicine.

Our second Research Topic in this series, Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II (https://fro.ntiers.in/14361) has over 8 accepted articles and further manuscripts currently under review. Due to the continued success of these Research Topics and the interest in the subject, we will launch a third volume on the same topic. Inferring cancer tissue-of-origin and molecular classification are two critical problems in personalized cancer therapy. It is known that there are about 5% cancers of unknown primary (CUP) site. These kinds of patients are under empirical chemotherapy, which leads to a very low survival rate. Thus, it is important to infer cancer tissue-of-origin. However, experimental methods usually fail to identify the exact tissue-of-origin even after the death of a patient, which provides a need for computational methods especially in the era of big biomedical data. Based on the finding that gene expressions of metastasis cancer cells are more similar to those of original tissue than metastasis tissue, there have been a few computational methods developed in this area. However, the accuracy of the methods is yet to be improved to assure a clinical usage. In addition to CUP, inferring cancer tissue-of-origin is also important in avoiding misdiagnosis even if the cancer origin is known.

This book constitutes the proceedings of the 16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020, held in Moscow, Russia, in December 2020. The 23 full papers and 18 short papers presented in this book were carefully reviewed and selected from 131 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data analysis and methodology.

This book describes translational cancer therapeutics and the way forward from clinical and molecular diagnosis to treatment. In addition, genomics alterations, microRNAs, and long non-coding RNAs translate precision medicine for the individualistic therapy of cancer patients. It describes the involvement of various pharmacogenetic factors in pharmacodynamic/pharmacokinetic (PD/PK) modulations of medicines. Indeed, the role of bioinformatics and biostatistics, considering the extensive data analysis serving precision medicine approaches, has also been entertained in the present book. Therefore, intended book demonstrates the successful medical evidence for the use of precision medicine in the treatment of cancer and its future clinical perspectives. It fills the gaps in cancer biology and precision medicine with its up-to-date content and well-designed chapters. It will serve as a valuable resource for science, medical students, and interdisciplinary researchers. It is a very welcome addition for the scientific community, research centers, and university-industry research collaborators to find out a complete capsular package about cancer drug targets, precision, and personalized medicine (including an introduction to cancer cell signaling, genomic alterations, miRNA targeting, pharmacogenetics, biomarkers, and metabolomics in precision medicine, etc.) at a single platform.

This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.

Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases