Zhiquan He
University of Missouri
12 Papers
77 Citations
Zhiquan He is an academic researcher from University of Missouri. The author has contributed to research in topics: Protein structure prediction & CASP. The author has an hindex of 7, co-authored 10 publications. Previous affiliations of Zhiquan He include Shenzhen University.
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Papers
MUFOLD: A new solution for protein 3D structure prediction
TL;DR: A systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein, and an efficient process was applied for iterative coarse‐grain model generation and evaluation at the Cα or backbone level, which shows significant and systematic improvement over previous methods.
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Protein structural model selection by combining consensus and single scoring methods.
TL;DR: A novel method called PWCom is presented, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca, which significantly improves over consensus G DT and single scoring methods.
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Transmembrane protein alignment and fold recognition based on predicted topology.
TL;DR: A novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment that is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition.
MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis
TL;DR: MUFOLD-DB integrates processed PDB sequence and structure data and multiple computational results, provides a friendly interface for users to retrieve, browse and download these data, and offers several useful functionalities to facilitate users' data operation.
A Fast Projection-Based Algorithm for Clustering Big Data.
TL;DR: It is shown that the MUFOLD-CL can serve as a valuable tool, at least may play a complementary role to other existing methods, for big data clustering, and comparison with K-Means method on very large data showed that the method could produce better accuracy and require less computational time.
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