BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectabi...
MOTIVATION: Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing different...
Enhancer is a DNA sequence of a genome that controls transcription of downstream target genes. Enhancers are known to be associated with certain epigenetic signatures. Machine learning tools, such as CSI-ANN, ChromHMM, and RFECS, were developed for p...
IEEE/ACM transactions on computational biology and bioinformatics
30951477
DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, there are several sequence-based deep learning models designed to predict DNA methylation states, whic...
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...
Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associa...
BACKGROUND: The three-dimensional (3D) structure of chromatins plays significant roles during cell differentiation and development. Hi-C and other 3C-based technologies allow us to look deep into the chromatin architectures. Many studies have suggest...
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and t...
IEEE/ACM transactions on computational biology and bioinformatics
30835229
Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary D...