MLACP: machine-learning-based prediction of anticancer peptides.
about
DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection.AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Unraveling the bioactivity of anticancer peptides as deduced from machine learningPrediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid FeaturesIdentifying Phage Virion Proteins by Using Two-Step Feature Selection MethodsiGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised treeA Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites
P2860
Q49403730-93573C20-A5D2-4BF2-B4C8-A1BF3ABF6E82Q50002018-9BAA3F15-16A5-495A-A900-D649088EAB0BQ52595216-D05F5D9B-F1BA-4605-B8ED-7E90D454E43BQ52608555-9A5B74B5-4CCB-4B08-B1E0-DFC1AD37972DQ57174709-B1B0E8B5-52F5-40A1-B167-E584B2D8BE59Q57319273-C2E6F850-77F3-445E-A42F-1CA6D954EECCQ58697072-0F4A41CD-068A-4F67-AD24-7DF5781FFE45Q58697614-272A55BC-392E-456F-B916-70F19E3B3F9BQ58722840-0C5D4748-FCB2-44E3-A17B-1F365F2C95B8
P2860
MLACP: machine-learning-based prediction of anticancer peptides.
description
2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
@zh-my
2017年学术文章
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2017年學術文章
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2017年學術文章
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name
MLACP: machine-learning-based prediction of anticancer peptides.
@en
MLACP: machine-learning-based prediction of anticancer peptides.
@nl
type
label
MLACP: machine-learning-based prediction of anticancer peptides.
@en
MLACP: machine-learning-based prediction of anticancer peptides.
@nl
prefLabel
MLACP: machine-learning-based prediction of anticancer peptides.
@en
MLACP: machine-learning-based prediction of anticancer peptides.
@nl
P2093
P2860
P356
P1433
P1476
MLACP: machine-learning-based prediction of anticancer peptides.
@en
P2093
Balachandran Manavalan
Myeong Ok Kim
Shaherin Basith
Tae Hwan Shin
P2860
P304
77121-77136
P356
10.18632/ONCOTARGET.20365
P407
P50
P577
2017-08-19T00:00:00Z