about
Recent Progress in Machine Learning-Based Methods for Protein Fold RecognitionBriefing in family characteristics of microRNAs and their applications in cancer research.CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency.PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only.Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set.Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine.Improved detection of DNA-binding proteins via compression technology on PSSM information.Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.Fast prediction of protein methylation sites using a sequence-based feature selection technique.A novel machine learning method for cytokine-receptor interaction prediction.UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification.A novel hierarchical selective ensemble classifier with bioinformatics application.SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learningConstruction, Model-Based Analysis, and Characterization of a Promoter Library for Fine-Tuned Gene Expression in Bacillus subtilisACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptidesThree-Dimensional Face Reconstruction Using Multi-View-Based Bilinear ModelAn Improved Protein Structural Classes Prediction Method by Incorporating Both Sequence and Structure InformationDeveloping a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics DataIdentifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanismM6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble LearningmAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representationEmpirical comparison and analysis of web-based cell-penetrating peptide prediction toolsACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptidesExploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple speciesDeep-Resp-Forest: A deep forest model to predict anti-cancer drug responseIdentification of expression signatures for non-small-cell lung carcinoma subtype classificationIterative feature representations improve N4-methylcytosine site predictionMinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy-defined energyPEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning
P50
Q28066913-5A219A89-77D1-4CEC-B72E-FE5EEAC303D8Q38129652-6784F059-7511-48B1-9EF3-DEEECE5E4606Q38823458-151C3F79-7CB9-49AE-91CA-7C39DEBF0019Q38976242-FE6261A4-5FDD-4623-9367-E71BC502BA0CQ40555764-1EE10043-FFC8-4DF1-83BE-9B7CFF3AFB7FQ42290571-762C1DD2-4899-48C9-A9BD-BE2128D7B865Q42367541-9E5936FD-D97F-40EE-A3F5-CE669F0D0A26Q45945533-8165C470-65E3-4849-8D63-746C2660F983Q45948462-398A0FB6-7D79-4E0B-99EA-85BE407196EBQ45952991-2BD09F70-575E-46E8-BB13-19FEE6A99D99Q47116051-097944BE-6484-43F6-B625-7BCD37F6ECF4Q47716490-14C903CC-88C0-4033-9B28-5FDA9892A4D2Q47956117-A0ED00AB-2982-4894-9B23-E988A30DB134Q50420545-63ABB219-F06F-4421-8081-03BBA7BA959FQ51093256-AC7EAD2A-D232-45A6-9023-0F5B099B4448Q57156105-B457D609-232B-4EC0-B799-9F9BF2647B89Q57175186-7C268449-9204-4817-9124-458EA9FD3D19Q62729907-03A4E4AB-A49F-42FB-83CE-337E35F018A8Q64273948-5F03BA89-1D82-4562-B6AC-4A5DAFFD25C3Q85363625-EC1BA2CC-11CD-4CDB-9C94-D5DE7503CEB5Q90441600-FB90C48C-95E8-49AD-8D56-3A5DAC3C36A6Q90539318-5462F8DD-72AB-4279-86AB-9665F9FBDD3AQ90777186-7EEEC048-CF8A-4D42-A533-68DC127A6FCAQ90778936-7A55028C-9B75-4564-8831-B108FB10E888Q91071271-FD672815-50B4-4F4C-A363-A7C74948CFE6Q91292376-ED3DFAE7-A3B8-4AEC-8D14-551AFC70706BQ91618176-79A10C34-6171-4018-96A5-F7CE11AB1641Q91649567-0D76120B-F027-4D11-A89E-C4AA6FF472EBQ91814774-119926FA-5AF3-40B5-8317-526E08FF2F10Q92106757-352DC67A-A90C-4505-8BF2-2A2B1D884795Q92297060-1E1A53A5-90CF-4F4D-8707-E5BEF6409412Q93153819-3332B1B0-157D-486E-9AB5-3B4D981001D9
P50
description
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
Leyi Wei
@ast
Leyi Wei
@en
Leyi Wei
@es
Leyi Wei
@nl
Leyi Wei
@sl
type
label
Leyi Wei
@ast
Leyi Wei
@en
Leyi Wei
@es
Leyi Wei
@nl
Leyi Wei
@sl
prefLabel
Leyi Wei
@ast
Leyi Wei
@en
Leyi Wei
@es
Leyi Wei
@nl
Leyi Wei
@sl
P106
P2456
P31
P496
0000-0003-1444-190X