scholarly article | Q13442814 |
P819 | ADS bibcode | 2017NatSR...716023W |
P6179 | Dimensions Publication ID | 1092730443 |
P356 | DOI | 10.1038/S41598-017-16397-Z |
P8608 | Fatcat ID | release_7mv23iucmvcunghe67uryfpiuu |
P932 | PMC publication ID | 5700192 |
P698 | PubMed publication ID | 29167570 |
P50 | author | Xinqi Gong | Q88143779 |
P2093 | author name string | Wei Wang | |
Jianxin Yin | |||
Yongxiao Yang | |||
P2860 | cites work | Information assessment on predicting protein-protein interactions | Q24796950 |
Computational prediction of protein interfaces: A review of data driven methods | Q26783912 | ||
Principles of protein-protein interactions | Q27860855 | ||
Prediction of protein-protein interaction sites in sequences and 3D structures by random forests | Q28474551 | ||
Progress and challenges in predicting protein interfaces | Q28602320 | ||
Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest | Q30648471 | ||
Evaluation of different biological data and computational classification methods for use in protein interaction prediction | Q31031427 | ||
Partner-aware prediction of interacting residues in protein-protein complexes from sequence data | Q31043905 | ||
A comparative study of different machine learning methods on microarray gene expression data | Q31150616 | ||
Support vector machines and kernels for computational biology | Q33381579 | ||
Principles of protein-protein recognition | Q33954341 | ||
Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. | Q34487825 | ||
PAIRpred: partner-specific prediction of interacting residues from sequence and structure | Q35045263 | ||
Learning interactions via hierarchical group-lasso regularization | Q36446528 | ||
History of protein-protein interactions: from egg-white to complex networks | Q38019869 | ||
Predicting protein interface residues using easily accessible on-line resources. | Q38386937 | ||
Predicting Protein-Protein Interactions from the Molecular to the Proteome Level | Q38807305 | ||
Predicting protein--protein interactions from primary structure | Q40710149 | ||
Diffusion kernel-based logistic regression models for protein function prediction | Q45158942 | ||
Prediction of protein-protein interaction sites using support vector machines | Q47869771 | ||
Prediction of protein-protein interactions using random decision forest framework | Q48470497 | ||
The Key–Lock Theory and the Induced Fit Theory | Q56144898 | ||
Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier | Q87212279 | ||
Random Forests | Q115707260 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P433 | issue | 1 | |
P407 | language of work or name | English | Q1860 |
P921 | main subject | machine learning | Q2539 |
P304 | page(s) | 16023 | |
P577 | publication date | 2017-11-22 | |
P1433 | published in | Scientific Reports | Q2261792 |
P1476 | title | Different protein-protein interface patterns predicted by different machine learning methods | |
P478 | volume | 7 |
Q99724618 | A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers |
Q57281082 | Neurocardiac regulation: From cardiac mechanisms to novel therapeutic approaches |
Q57031875 | Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction |