Shen Group's project "Artificial Intelligence Algorithm Development for Biological Medical Big Data Understanding and Its Online Prediction Application Systems" has been elected to the Final list of SAIL award of Artificial Intelligence World Innovations 2018.
Enriched RNA-protein binding motifs revealed by new iDeepE model. RNA-binding proteins take over 5–10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. We present a deep learning-based method iDeepE to predict the RBP binding sites from sequences alone by fusing the local multi-channel convolutional neural networks and global convolutional neural networks. It is able to mine new binding motifs from big data pool efficiently. This study is published in Pan and Shen, Bioinformatics, 2018.
Inter-residue contacts in proteins have been widely acknowledged to be valuable for protein 3D structure prediction. Accurate prediction of long-range transmembrane inter-helix residue contacts can significantly improve the quality of simulated membrane protein models. We found that deep convolutional neural network can mine latent residue contact patterns and thus improve inter-helix residue contact prediction. The new MemBrain is a two-stage inter-helix contact predictor. The first stage takes sequence-based features as inputs and outputs coarse contact probabilities for each residue pair, which will be further fed into convolutional neural network together with predictions from three direct-coupling analysis approaches in the second stage. The study is published in Jing Yang and Hong-Bin Shen, Bioinformatics, 2018, 34: 230-238.
How to measure the resolution of a reconstructed 3D density map is an important problem of the Single-Particle Reconstruction (SPR) of cryo-EM images. It plays a critical role for promoting methodology development of SPR and structural biology. Due to there is no benchmark map in a new structure generation, how to realize the resolution estimation of a new map is still an open question. We proposed a new self-reference-based resolution estimation protocol SRes, which only requires a single reconstructed 3D map for the purpose of resolution measurement. The core idea in SRes is performing a multi-scale spectral analysis on the map through multiple size-variable masks segmenting the map. The new SRes approach has provided a new routine for measuring the resolution from a single density map. This study is published in Yang et.al, Journal of Chemical Information and Modeling, 2018.
AdipoCount, a new obesity cell segmentation and counting system. Obesity has spread worldwide and become a common health problem in modern society. One typical feature of obesity is the excessive accumulation of fat in adipocytes, which occurs through the following two physiological phenomena: hyperplasia (increase in quantity) and hypertrophy (increase in size) of adipocytes. In clinical and scientific research, the accurate quantification of the number and diameter of adipocytes is necessary for assessing obesity. We have developed a new bioimage-understanding based automatic adipocyte counting system, AdipoCount, which is accurate and supports further manual interaction. The outputs of this system are the labels and the statistical data of all adipose cells in the image. AdipoCount is published in Zhi et.al, Frontiers in Physiology, 2018, 9: 85.