Bingxiang Liu
Jingdezhen Ceramic Institute
11 Papers
158 Citations
Bingxiang Liu is an academic researcher from Jingdezhen Ceramic Institute. The author has contributed to research in topics: Pseudo amino acid composition & Wavelet transform. The author has an hindex of 9, co-authored 10 publications.
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Papers
iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.
TL;DR: A predictor called iCar-PseCp is developed by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset.
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iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets
TL;DR: A new predictor, called iPPBS-Opt, is proposed, in which the K-Nearest Neighbors Cleaning and Inserting Hypothetical Training Samples (IHTS) treatments are used to optimize the training dataset and the ensemble voting approach to select the most relevant features is used to formulate the statistical samples.
Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition.
TL;DR: Cross-validation tests indicate that the new predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment, is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach.
Bagging-based spectral clustering ensemble selection
TL;DR: A novel clustering ensemble method, SELective Spectral Clustering Ensemble (SELSCE), is proposed and the experimental results demonstrate that the proposed algorithm can achieve a better result than the traditional clusteringsemble methods.
106
Prediction of Protein–Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests
TL;DR: The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of 0.85, and can be a usefully supplementary tool for PPI prediction.
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