A5034490149- Gene expression and cancer classification
- Single-cell and spatial transcriptomics
- Genomics and Phylogenetic Studies
- Genomics and Chromatin Dynamics
- Digital Imaging for Blood Diseases
- Bioinformatics and Genomic Networks
- Extracellular vesicles in disease
- Computational Drug Discovery Methods
- Chromosomal and Genetic Variations
- Cell Image Analysis Techniques
Northwest A&F University
2021-2023
Abstract Motivation Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure cells. Although various experimental techniques been developed detect loops, they found be time-consuming costly. Nowadays, sequence-based computational methods can capture significant features help predict loops. However, these low performance poor...
Single-cell Hi-C data are a common source for studying the differences in three-dimensional structure of cell chromosomes. The development single-cell technology makes it possible to obtain batches data. How quickly and effectively discriminate types has become one hot research field. However, existing computational methods predict based on found be low accuracy. Therefore, we propose high accuracy classification algorithm, called scHiCStackL, In our work, first improve preprocessing method...
Abstract Single-cell clustering is the most significant part of single-cell RNA sequencing (scRNA-seq) data analysis. One main issue facing scRNA-seq noise and sparsity, which poses a great challenge for advance high-precision algorithms. This study adopts cellular markers to identify differences between cells, contributes feature extraction single cells. In this work, we propose algorithm-SCMcluster (single-cell cluster using marker genes). algorithm integrates two cell databases(CellMarker...
The chromatin loops in the three-dimensional (3D) structure of chromosomes are essential for regulation gene expression. Despite fact that high-throughput capture techniques can identify 3D chromosomes, loop detection utilizing biological experiments is arduous and time-consuming. Therefore, a computational method required to detect loops. Deep neural networks form complex representations Hi-C data provide possibility processing datasets. we propose bagging ensemble one-dimensional...
Abstract The development of sequencing technology has promoted the expansion cancer genome data. It is necessary to identify pathogenesis at molecular level and explore reliable treatment methods precise drug targets in by identifying carcinogenic functional modules massive multi‐omics However, there are still limitations driver utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, prostate cancer. Firstly, high mutual exclusivity, coverage,...