ArticleScientific reports2017
WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- CMash: fast, multi-resolution estimation of k-mer-based Jaccard and containment indices.Bioinformatics (Oxford, England) · 2022Article
- TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.Nucleic acids research · 2022Article
- Cancer CRC: A Comprehensive Cancer Core Transcriptional Regulatory Circuit Resource and Analysis Platform.Frontiers in oncology · 2021Article
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.BMC bioinformatics · 2019Article
- FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances.Journal of visualized experiments : JoVE · 2018Article
- Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production.Frontiers in bioengineering and biotechnology · 2018Review
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3 authors.
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Abstract
Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a "global" optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.
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