| 1 |
Dindhoria K, Monga I, Thind AS. Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq. Funct Integr Genomics 2022. [DOI: 10.1007/s10142-022-00915-y] [Reference Citation Analysis]
|
| 2 |
Xiang M, Luo W, Hou J, Tao W. Attention-based Feature Fusion for Reconstructing Gene-Regulatory Interactions. 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) 2022. [DOI: 10.1109/dsaa54385.2022.10032449] [Reference Citation Analysis]
|
| 3 |
Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.08.003] [Reference Citation Analysis]
|
| 4 |
Yang G, Wang C, Yang G. Inference of Gene Regulatory Networks with Local Lasso Path Consistency Algorithm Based on Conditional Mutual Information. 2022 13th Asian Control Conference (ASCC) 2022. [DOI: 10.23919/ascc56756.2022.9828308] [Reference Citation Analysis]
|
| 5 |
Zhang Y, He Y, Chen Q, Yang Y, Gong M. Fusion prior gene network for high reliable single-cell gene regulatory network inference. Computers in Biology and Medicine 2022;143:105279. [DOI: 10.1016/j.compbiomed.2022.105279] [Reference Citation Analysis]
|
| 6 |
Li K, Yan C, Li C, Chen L, Zhao J, Zhang Z, Bao S, Sun J, Zhou M. Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data. Molecular Therapy - Nucleic Acids 2022;27:404-411. [DOI: 10.1016/j.omtn.2021.12.009] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 7 |
Zhao M, He W, Tang J, Zou Q, Guo F. A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. Briefings in Bioinformatics. [DOI: 10.1093/bib/bbab568] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 8 |
Yang B. Gene Regulatory Network Identification based on Forest Graph-embedded Deep Feedforward Network. 2021 6th International Conference on Cloud Computing and Internet of Things 2021. [DOI: 10.1145/3493287.3493297] [Reference Citation Analysis]
|
| 9 |
Choudhary K, Meng EC, Diaz-Mejia JJ, Bader GD, Pico AR, Morris JH. scNetViz: from single cells to networks using Cytoscape. F1000Res 2021;10:ISCB Comm J-448. [PMID: 34912541 DOI: 10.12688/f1000research.52460.1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
|
| 10 |
Akers K, Murali T. Gene regulatory network inference in single-cell biology. Current Opinion in Systems Biology 2021;26:87-97. [DOI: 10.1016/j.coisb.2021.04.007] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
|
| 11 |
Turki T, Taguchi YH. Discriminating the single-cell gene regulatory networks of human pancreatic islets: A novel deep learning application. Comput Biol Med 2021;132:104257. [PMID: 33740535 DOI: 10.1016/j.compbiomed.2021.104257] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
|
| 12 |
Nayak R, Hasija Y. A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines. Genomics 2021;113:606-19. [PMID: 33485955 DOI: 10.1016/j.ygeno.2021.01.007] [Cited by in Crossref: 18] [Cited by in F6Publishing: 22] [Article Influence: 9.0] [Reference Citation Analysis]
|
| 13 |
Turki T, Taguchi Y. Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application.. [DOI: 10.1101/2020.08.30.273839] [Reference Citation Analysis]
|
