CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

ABSTRACT:

Background

Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent years, convolutional neural networks (CNNs) have succeeded in TF-DNA binding prediction, but existing DL methods' accuracy needs to be improved and convolution function in TF-DNA binding prediction should be further explored.

Results

We develop a cascaded convolutional neural network model named CacPred to predict TF-DNA binding on 790 Chromatin immunoprecipitation-sequencing (ChIP-seq) datasets and seven ChIP-nexus (chromatin immunoprecipitation experiments with nucleotide resolution through exonuclease, unique barcode, and single l

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11916463 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature