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Items: 1 to 20 of 97


A High Efficient Biological Language Model for Predicting Protein⁻Protein Interactions.

Wang Y, You ZH, Yang S, Li X, Jiang TH, Zhou X.

Cells. 2019 Feb 3;8(2). pii: E122. doi: 10.3390/cells8020122.


Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Li H, Gong XJ, Yu H, Zhou C.

Molecules. 2018 Aug 1;23(8). pii: E1923. doi: 10.3390/molecules23081923.


Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.

Huang YA, You ZH, Chen X, Yan GY.

BMC Syst Biol. 2016 Dec 23;10(Suppl 4):120. doi: 10.1186/s12918-016-0360-6.


Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).

Foffi G, Pastore A, Piazza F, Temussi PA.

Phys Biol. 2013 Aug;10(4):040301. Epub 2013 Aug 2.


Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.

Chiu B, Pyysalo S, Vulić I, Korhonen A.

BMC Bioinformatics. 2018 Feb 5;19(1):33. doi: 10.1186/s12859-018-2039-z.


Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.

You ZH, Chan KC, Hu P.

PLoS One. 2015 May 6;10(5):e0125811. doi: 10.1371/journal.pone.0125811. eCollection 2015.


Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features.

Wang T, Li L, Huang YA, Zhang H, Ma Y, Zhou X.

Molecules. 2018 Apr 4;23(4). pii: E823. doi: 10.3390/molecules23040823.


Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks.

Li X, Wang H, He H, Du J, Chen J, Wu J.

BMC Bioinformatics. 2019 Feb 1;20(1):62. doi: 10.1186/s12859-019-2617-8.


Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network.

Wang YB, You ZH, Li X, Jiang TH, Chen X, Zhou X, Wang L.

Mol Biosyst. 2017 Jun 27;13(7):1336-1344. doi: 10.1039/c7mb00188f.


Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction.

Ananthasubramanian S, Metri R, Khetan A, Gupta A, Handen A, Chandra N, Ganapathiraju M.

Microb Inform Exp. 2012 Mar 21;2:4. doi: 10.1186/2042-5783-2-4.


RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.

An JY, You ZH, Meng FR, Xu SJ, Wang Y.

Int J Mol Sci. 2016 May 18;17(5). pii: E757. doi: 10.3390/ijms17050757.


An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information.

You ZH, Huang W, Zhang S, Huang YA, Yu CQ, Li LP.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov 20. doi: 10.1109/TCBB.2018.2882423. [Epub ahead of print]


Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

An JY, Meng FR, You ZH, Chen X, Yan GY, Hu JP.

Protein Sci. 2016 Oct;25(10):1825-33. doi: 10.1002/pro.2991. Epub 2016 Aug 9.


Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence.

Huang YA, You ZH, Gao X, Wong L, Wang L.

Biomed Res Int. 2015;2015:902198. doi: 10.1155/2015/902198. Epub 2015 Oct 28.


Predicting protein-protein interactions based only on sequences information.

Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, Li Y, Jiang H.

Proc Natl Acad Sci U S A. 2007 Mar 13;104(11):4337-41. Epub 2007 Mar 5.


Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

Wang L, You ZH, Huang DS, Zhou F.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Oct 5. doi: 10.1109/TCBB.2018.2874267. [Epub ahead of print]


Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease.

Kim S, Kim J, Chun HW.

Int J Environ Res Public Health. 2018 Aug 15;15(8). pii: E1750. doi: 10.3390/ijerph15081750.


Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

Asgari E, Mofrad MR.

PLoS One. 2015 Nov 10;10(11):e0141287. doi: 10.1371/journal.pone.0141287. eCollection 2015.

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