Li Y, et al. (2022) Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation. Comput Math Methods Med 2022:7191684 PMID:35242211
Wang Y, et al. (2022) SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks. Biomedicines 10(7) PMID:35884848
Li Y, et al. (2021) Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information. Sci Rep 11(1):16910 PMID:34413375
Pan J, et al. (2021) Prediction of Protein-Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform. Front Genet 12:745228 PMID:34616437
Jia LN, et al. (2020) NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information. Evol Bioinform Online 16:1176934320984171 PMID:33488064
Zhan XK, et al. (2020) Using Random Forest Model Combined With Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence. Evol Bioinform Online 16:1176934320934498 PMID:32655275
An JY, et al. (2019) Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine. Evol Bioinform Online 15:1176934319844522 PMID:31080346
Chen ZH, et al. (2019) Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform. Int J Mol Sci 20(4) PMID:30795499
Li Y, et al. (2019) An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model. Int J Mol Sci 20(14) PMID:31319578
Li LP, et al. (2018) PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation. Int J Mol Sci 19(4) PMID:29596363
Wang L, et al. (2018) Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions. Sci Rep 8(1):12874 PMID:30150728
You ZH, et al. (2018) An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information. IEEE/ACM Trans Comput Biol Bioinform PMID:30475726
Wang L, et al. (2017) An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences. Oncotarget 8(3):5149-5159 PMID:28029645
Wang YB, et al. (2017) Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol Biosyst 13(7):1336-1344 PMID:28604872
Wang YB, et al. (2017) Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM. Molecules 22(8) PMID:28820478
Wen YT, et al. (2017) Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network. J Theor Biol 430:9-20 PMID:28625475
An JY, et al. (2016) Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences. Biomed Res Int 2016:4783801 PMID:27314023
An JY, et al. (2016) RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences. Int J Mol Sci 17(5) PMID:27213337
An JY, et al. (2016) Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information. Mol Biosyst 12(12):3702-3710 PMID:27759121
An JY, et al. (2016) Identification of self-interacting proteins by exploring evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix. Oncotarget 7(50):82440-82449 PMID:27732957
An JY, et al. (2016) Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model. Protein Sci 25(10):1825-33 PMID:27452983
Gao ZG, et al. (2016) Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM. Biomed Res Int 2016:4563524 PMID:27437399
Huang YA, et al. (2016) Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics 17(1):184 PMID:27112932
Li ZW, et al. (2016) Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics. Int J Mol Sci 17(9) PMID:27571061
Huang YA, et al. (2015) Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence. Biomed Res Int 2015:902198 PMID:26634213
Wong L, et al. (2015) Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors. Int J Mol Sci 17(1) PMID:26712745
You ZH, et al. (2015) Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines. Biomed Res Int 2015:867516 PMID:26000305
You ZH, et al. (2015) Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. PLoS One 10(5):e0125811 PMID:25946106
You ZH, et al. (2014) Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinformatics 15 Suppl 15(Suppl 15):S9 PMID:25474679
You ZH, et al. (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14 Suppl 8(Suppl 8):S10 PMID:23815620
Lei YK, et al. (2012) Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC Bioinformatics 13 Suppl 7(Suppl 7):S3 PMID:22595000
You ZH, et al. (2010) A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinformatics 11:343 PMID:20573270
You ZH, et al. (2010) Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 26(21):2744-51 PMID:20817744