The journal continues to take on big and tough issues as exemplified by the November 2018 special issue "Machine Learning in Health and Biomedicine." As computational power increases exponentially, the capacity to (more affordably) handle, store, and analyze "big data" using machine learning (ML) will revolutionize science and medicine. The power of ML is to find patterns among variables in large data sets rather than being programmed with rules. Models become more complex when they move from supervised (input and outputs have labels) to unsupervised (no labels), and when they move from linear regression with decision trees to neural networks ( 3 neural networks is termed deep learning). As the complexity increases so does one's ability to "interpret" the data.

Note: This Editorial is appearing in Speaking of Medicine ahead of print. The final version will appear in PLOS Medicine at the end of December. PLOS Medicine Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation. Artificial Intelligence (AI) as a field emerged in the 1960s when practitioners across the engineering and cognitive sciences began to study how to develop computational technologies that, like people, can perform tasks such as sensing, learning, reasoning, and taking action. Early AI systems relied heavily on expert-derived rules for replicating how people would approach these tasks.

PLOS Medicine, PLOS Computational Biology and PLOS ONE announce a cross-journal Call for Papers for high-quality research that applies or develops machine learning methods for improvement of human health. The team of Guest Editors for this Collection seeks research with direct clinical and health policy implications, studies that elucidate biological processes underlying health and disease, innovations in machine learning methodology and data provision, and other advances in the field. Research accepted for publication in PLOS Medicine will appear in a Special Issue to be published in late Fall 2018, along with commentary from leading experts in the field. The broader Collection, comprising all articles published in PLOS Computational Biology, PLOS ONE and PLOS Medicine, will launch in late Fall and continue into 2019. Articles must be submitted by May 25, 2018.

PLOS Medicine, PLOS Computational Biology and PLOS ONE are excited to announce a cross-journal Call for Papers for high-quality research that applies or develops machine learning methods for improvement of human health. The team of Guest Editors for this Collection seeks research with direct clinical and health policy implications, studies that elucidate biological processes underlying health and disease, innovations in machine learning methodology and data provision, and other advances in the field. Research accepted for publication in PLOS Medicine will appear in a Special Issue to be published in late Fall 2018, along with commentary from leading experts in the field. The broader Collection, comprising all articles published in PLOS Computational Biology, PLOS ONE and PLOS Medicine, will launch in late Fall and continue into 2019. Articles must be submitted by May 25, 2018.

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.