Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

A month ago we finished Why R? 2020 conference. We had an pleasure to host Roger Bivand, a professor at Norwegian School of Economics and Member of R Foundation. This post contains a biography of the speaker and an abstract of his talk: Applied Spatial Data Analysis with R – retrospect and prospect. When we began over 20 years ago, spatial data was usually found in proprietary software, usually geographical information systems, and most positional data was very hard to acquire. Statistics for spatial data existed, but largely without convenient access to positional data.