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Prediction of the transcription factor binding sites with meta-learning
Jing, Fang1; Zhang, Shao-Wu1,5; Zhang, Shihua2,3,4,6
Source PublicationMETHODS
AbstractWith the accumulation of ChIP-seq data, convolution neural network (CNN)-based methods have been proposed for predicting transcription factor binding sites (TFBSs). However, biological experimental data are noisy, and are often treated as ground truth for both training and testing. Particularly, existing classification methods ignore the false positive and false negative which are caused by the error in the peak calling stage, and therefore, they can easily overfit to biased training data. It leads to inaccurate identification and inability to reveal the rules of governing protein-DNA binding. To address this issue, we proposed a meta learning-based CNN method (namely TFBS_MLCNN or MLCNN for short) for suppressing the influence of noisy labels data and accurately recognizing TFBSs from ChIP-seq data. Guided by a small amount of unbiased meta-data, MLCNN can adaptively learn an explicit weighting function from ChIP-seq data and update the parameter of classifier simultaneously. The weighting function overcomes the influence of biased training data on classifier by assigning a weight to each sample according to its training loss. The experimental results on 424 ChIP-seq datasets show that MLCNN not only outperforms other existing state-of-the-art CNN methods, but can also detect noisy samples which are given the small weights to suppress them. The suppression ability to the noisy samples can be revealed through the visualization of samples' weights. Several case studies demonstrate that MLCNN has superior performance to others.
KeywordConvolution neural network Transcription factor binding sites Meta learning Noisy labels data
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[61873202,62173271] ; National Natural Science Foundation of China[61621003] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDPB17] ; National Ten Thousand Talent Pro-gram for Young Top-notch Talents[QYZDB-SSW-SYS008] ; CAS Frontier Science Research Key Project for Top Young Scientist
WOS Research AreaBiochemistry & Molecular Biology
WOS SubjectBiochemical Research Methods ; Biochemistry & Molecular Biology
WOS IDWOS:000809934400008
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Document Type期刊论文
Corresponding AuthorZhang, Shao-Wu; Zhang, Shihua
Affiliation1.Northwestern Polytech Univ, Sch Automat, MOE Key Lab Informat Fus Technol, Xi'an 710072, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, CEMS,RCSDS, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Peoples R China
5.Northwestern Polytech Univ, Sch Automat, Xi'an, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Jing, Fang,Zhang, Shao-Wu,Zhang, Shihua. Prediction of the transcription factor binding sites with meta-learning[J]. METHODS,2022,203:207-213.
APA Jing, Fang,Zhang, Shao-Wu,&Zhang, Shihua.(2022).Prediction of the transcription factor binding sites with meta-learning.METHODS,203,207-213.
MLA Jing, Fang,et al."Prediction of the transcription factor binding sites with meta-learning".METHODS 203(2022):207-213.
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