标题
Systematic auditing is essential to debiasing machine learning in biology
作者
关键词
-
出版物
Communications Biology
Volume 4, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-02-11
DOI
10.1038/s42003-021-01674-5
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction
- (2019) Zhonghao Liu et al. Scientific Reports
- DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
- (2019) Ingoo Lee et al. PLoS Computational Biology
- Deep learning for inferring gene relationships from single-cell expression data
- (2019) Ye Yuan et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Modeling polypharmacy side effects with graph convolutional networks
- (2018) Marinka Zitnik et al. BIOINFORMATICS
- Using deep learning to model the hierarchical structure and function of a cell
- (2018) Jianzhu Ma et al. NATURE METHODS
- MHCflurry: Open-Source Class I MHC Binding Affinity Prediction
- (2018) Timothy J. O'Donnell et al. Cell Systems
- AI can be sexist and racist — it’s time to make it fair
- (2018) James Zou et al. NATURE
- Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes
- (2018) Weilong Zhao et al. PLoS Computational Biology
- NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
- (2017) Vanessa Jurtz et al. JOURNAL OF IMMUNOLOGY
- Evolutionary profiles improve protein–protein interaction prediction from sequence
- (2015) Tobias Hamp et al. BIOINFORMATICS
- More challenges for machine-learning protein interactions
- (2015) Tobias Hamp et al. BIOINFORMATICS
- Toward more realistic drug-target interaction predictions
- (2014) T. Pahikkala et al. BRIEFINGS IN BIOINFORMATICS
- A Proteome-Scale Map of the Human Interactome Network
- (2014) Thomas Rolland et al. CELL
- Large-scale prediction of human kinase–inhibitor interactions using protein sequences and molecular topological structures
- (2013) Dong-Sheng Cao et al. ANALYTICA CHIMICA ACTA
- Efficient regularized least-squares algorithms for conditional ranking on relational data
- (2013) Tapio Pahikkala et al. MACHINE LEARNING
- Flaws in evaluation schemes for pair-input computational predictions
- (2012) Yungki Park et al. NATURE METHODS
- TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules
- (2012) Lianming Zhang et al. PLoS One
- LIBSVM
- (2012) Chih-Chung Chang et al. ACM Transactions on Intelligent Systems and Technology
- Revisiting the negative example sampling problem for predicting protein-protein interactions
- (2011) Y. Park et al. BIOINFORMATICS
- Navigating the kinome
- (2011) James T Metz et al. Nature Chemical Biology
- Simple sequence-based kernels do not predict protein-protein interactions
- (2010) J. Yu et al. BIOINFORMATICS
- Large-Scale Prediction of Human Protein−Protein Interactions from Amino Acid Sequence Based on Latent Topic Features
- (2010) Xiao-Yong Pan et al. JOURNAL OF PROTEOME RESEARCH
- The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding
- (2009) Hao Zhang et al. BIOINFORMATICS
- Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior
- (2009) Yohan Kim et al. BMC BIOINFORMATICS
- NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
- (2009) Morten Nielsen et al. BMC BIOINFORMATICS
- Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences
- (2008) Yanzhi Guo et al. NUCLEIC ACIDS RESEARCH
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