Jack Hanson

TL;DR: This work has implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction, and initial studies indicate that the method is more accurate in predicting functional sites in disordered regions.

TL;DR: The time has come to finish off the final stretch of the long march towards protein secondary structure prediction as more powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques forsecondary structure prediction.

TL;DR: The authors overcome the limited availability of high-resolution 3D RNA structures for model training limits RNA secondary structure prediction by pre-training a DNN on a large set of predicted RNA structures and using transfer learning with high- resolution structures.

TL;DR: This work proposes a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two- dimensional evolutionary coupling-based information.

TL;DR: An ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, are leveraged to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles, half-sphere exposure, contact numbers, and solvent accessible surface area (ASA).