| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data
|
Wang, Dingjie
|
|
|
189 |
C |
p. 54-64
|
artikel
|
| 2 |
Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map
|
Kunz, Timothy
|
|
|
189 |
C |
p. 12-21
|
artikel
|
| 3 |
CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning
|
Shang, Jiayu
|
|
|
189 |
C |
p. 95-103
|
artikel
|
| 4 |
Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder (RE-VAE) model
|
Hu, Ruifeng
|
|
|
189 |
C |
p. 44-53
|
artikel
|
| 5 |
Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data
|
Chen, Yingyu
|
|
|
189 |
C |
p. 34-43
|
artikel
|
| 6 |
Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions
|
Wang, Hao
|
|
|
189 |
C |
p. 22-33
|
artikel
|
| 7 |
Editorial Board
|
|
|
|
189 |
C |
p. ii
|
artikel
|
| 8 |
Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data
|
Xu, Fan
|
|
|
189 |
C |
p. 65-73
|
artikel
|
| 9 |
FreeHi-C spike-in simulations for benchmarking differential chromatin interaction detection
|
Zheng, Ye
|
|
|
189 |
C |
p. 3-11
|
artikel
|
| 10 |
Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer’s disease from multi-dimensional genomic data
|
Shao, Wei
|
|
|
189 |
C |
p. 86-94
|
artikel
|
| 11 |
Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer
|
Tong, Li
|
|
|
189 |
C |
p. 74-85
|
artikel
|
| 12 |
Machine learning for the analysis of multi-omics data
|
Sun, Yanni
|
|
|
189 |
C |
p. 1-2
|
artikel
|
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