Mark Gerstein

TL;DR: In this paper, the authors compare two possible potentials for urea; one based directly on a parameterization for proteins and another generated from ab initio, quantum calculations, and find that both potentials reproduce essentially the same observed water structure (as evident in radial distribution functions).

TL;DR: Whole-genome expression profiles provide a rich new data-trove for bioinformatics and initial analyses of the profiles have included clustering and cross-referencing to 'external' information on protein structure and function.

TL;DR: In this article, the authors compared the evolutionary dynamics between the Muridae and Hominidae and found that the divergence times between the two families are similar in divergence times to each other, and that the relative rates of nucleotide change and feature turnover in both neutral and functional sequences of the two groups are similar.

TL;DR: Some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE are explained.

TL;DR: The major challenge facing biologists in the next decade will be to ‘‘finish the job’’, that is, to ascribe a function to each of the proteins that have been discovered.