Cancer metabolomics is a rapidly evolving field that aims for a comprehensive dissection of the metabolic phenotypes and functional network of metabolites in human cancers. State of the art metabolomics tools have been developed and applied to studying cancer metabolism and developing metabolic targets for improved diagnosis, prognosis and therapeutic treatment of human cancers. Chapters are written by subject experts in the field of cancer metabolomics with cross-disciplinary contributions. Coverage includes advanced metabolomics technologies and methodologies, including chemical isotope labelling liquid chromatography - mass spectrometry, capillary ion chromatography - mass spectrometry, 2-D gas chromatography – mass spectrometry, capillary electrophoresis – mass spectrometry, nuclear magnetic resonance spectroscopy, shotgun lipidomics, tracer-based metabolomics, microbial metabolomics, mass spectrometry imaging for single cell metabolomics and functional metabolomics. In addition, the book highlights new discoveries in cancer metabolism such as hypoxia inducible factor pathway, isocitrate dehydrogenase 1 mutation and oncometabolites. Finally, contributors focus on the translational applications of metabolomics in human cancers such as glioma, head and neck cancer, and gastric cancer. This new volume will be a unique reference source for cancer researchers and promote applications of metabolomics in understanding cancer metabolism.

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Giving a fresh, substantial and in-depth overview of the topic, this book brings together the latest results in the field of metabolomics. It comprehensively presents the current state of the metabolomics field by underscoring experimental methods, analysis techniques, standardization practices, and advances in specific model systems. As a result, it helps to significantly broaden our perspective on the principles and strategies underpinning this emerging field.

This book constitutes the refereed joint proceedings of the Third International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, the Fourth International Workshop on Breast Image Analysis, BIA 2018, and the First International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 5 full papers (out of 10 submissions) presented at RAMBO, the 9 full papers (out of 18 submissions) presented at BIA, and the 20 full papers (out of 21 submissions) presented at TIA were carefully reviewed and selected. The RAMBO papers cover aspects of medical imaging where motion plays a role in the image formation or analysis. The BIA papers deal with topics such as computer-aided detection and diagnosis of breast cancer, quantitative analysis of breast imaging modalities, and large scale breast image screening and analysis. The TIA papers cover aspects of image analysis research for lung and cardiac diseases including segmentation, registration, quantification, modeling of the image acquisition process, visualization, validation, statistical modeling, biophysical lung modeling (computational anatomy), deep learning and novel applications.

Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.