Jian Peng

TL;DR: This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling.

TL;DR: DTINet is introduced, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations, which accurately explains the topological properties of individual nodes in the heterogeneous network.

TL;DR: This work functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays, suggesting that disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

TL;DR: DeepCNF as mentioned in this paper is a deep learning extension of Conditional Neural Fields (CNF), which is an integration of CRF and shallow neural networks, which can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF.

TL;DR: By choosing the prior properly, both BP and MF perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.