Hasil untuk "Biology"

M. Giacomello, Aswin Pyakurel, Christina Glytsou et al.

S. Boeynaems, S. Alberti, N. Fawzi et al.

Cellular compartments and organelles organize biological matter. Most well-known organelles are separated by a membrane boundary from their surrounding milieu. There are also many so-called membraneless organelles and recent studies suggest that these organelles, which are supramolecular assemblies of proteins and RNA molecules, form via protein phase separation. Recent discoveries have shed light on the molecular properties, formation, regulation, and function of membraneless organelles. A combination of techniques from cell biology, biophysics, physical chemistry, structural biology, and bioinformatics are starting to help establish the molecular principles of an emerging field, thus paving the way for exciting discoveries, including novel therapeutic approaches for the treatment of age-related disorders.

R. Loos, G. Yeo

The prevalence of obesity has tripled over the past four decades, imposing an enormous burden on people’s health. Polygenic (or common) obesity and rare, severe, early-onset monogenic obesity are often polarized as distinct diseases. However, gene discovery studies for both forms of obesity show that they have shared genetic and biological underpinnings, pointing to a key role for the brain in the control of body weight. Genome-wide association studies (GWAS) with increasing sample sizes and advances in sequencing technology are the main drivers behind a recent flurry of new discoveries. However, it is the post-GWAS, cross-disciplinary collaborations, which combine new omics technologies and analytical approaches, that have started to facilitate translation of genetic loci into meaningful biology and new avenues for treatment. In this Review, Loos and Yeo summarize our current understanding of the genetic underpinnings of monogenic and polygenic obesity. They highlight the commonalities revealed by recent studies and discuss the implications for treatment and prediction of obesity risk.

T. Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones et al.

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as can legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at the bench and bedside with the potential to transform several areas of biology and medicine.

M. V. Vander Heiden, R. Deberardinis

J. Irwin, T. Sterling, Michael M. Mysinger et al.

ZINC is a free public resource for ligand discovery. The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biological activity, physical property, vendor, catalog number, name, and CAS number. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

U. Alon

G. A. Mulligan

M. Cooper, T. Fehniger, M. Caligiuri

G. Desiraju, T. Steiner

D. Kendall

X. Xia, Z. Xie

R. Marcus, N. Sutin

J. Lambeth

F. Neidhardt, J. Ingraham

L. Hartwell, J. Hopfield, S. Leibler et al.

C. Kolar, D. Lodge

A. Jaffe, Alan Hall

N. Peppas, J. Z. Hilt, A. Khademhosseini et al.

T. Sota