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Yuedong Yang - Google Scholar
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and … Capturing non-local...
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Randomized, Double-Blind, Phase II Study of Ruxolitinib or ... - PubMed
2015年12月1日 · Purpose: Patients with advanced pancreatic adenocarcinoma have a poor prognosis and limited second-line treatment options. Evidence suggests a role for the Janus kinase (JAK)/signal transducer and activator of transcription pathway in the pathogenesis and clinical course of pancreatic cancer.
Reaching alignment-profile-based accuracy in predicting protein ...
2022年5月9日 · Here we show that a method called SPOT-1D-LM combines traditional one-hot encoding with the embeddings from two different language models (ProtTrans and ESM-1b) for the input and yields a leap in accuracy over single-sequence-based techniques in predicting protein 1D secondary and tertiary structural properties, including backbone torsion angles...
Accurate prediction of protein contact maps by coupling
2018年12月1日 · Here, we propose 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.
SPOT-Contact-LM: improving single-sequence-based prediction of …
2022年4月4日 · Bioinformatics, 34, 4039–4045. [Google Scholar] Hanson J. et al. (2019) Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics, 35, 2403–2410.
Accurate prediction of protein contact maps by coupling residual …
2018年6月19日 · Here, we propose 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.