论文信息 - TF-MoDISco v0.4.4.2-alpha: Technical Note

TF-MoDISco v0.4.4.2-alpha: Technical Note

TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This paper describes the methods behind TF-MoDISco version 0.4.4.2-alpha (available at this https URL).

[1] Vincent A. Traag,et al. From Louvain to Leiden: guaranteeing well-connected communities , 2018, Scientific Reports.

[2] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.

[3] Sean C. Bendall,et al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[4] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.

[5] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.

[6] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[7] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.

[8] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .

[9] Yanjun Qi,et al. Deep Motif: Visualizing Genomic Sequence Classifications , 2016, ArXiv.