How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

MLA

Quan, Lijun, et al. “How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 2, Mar. 2023, pp. 1594–99. EBSCOhost, https://doi.org/10.1109/TCBB.2022.3170343.

APA

Quan, L., Chu, X., Sun, X., Wu, T., & Lyu, Q. (2023). How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2), 1594–1599. https://doi.org/10.1109/TCBB.2022.3170343

Chicago

Quan, Lijun, Xiaomin Chu, Xiaoyu Sun, Tingfang Wu, and Qiang Lyu. 2023. “How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 (2): 1594–99. doi:10.1109/TCBB.2022.3170343.