Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana
Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co‐function netw...
Saved in:
Main Authors: | Hansen, Bjoern Oest, Meyer, Etienne H., Ferrari, Camilla, Vaid, Neha, Movahedi, Sara, Vandepoele, Klaas, Nikoloski, Zoran, Mutwil, Marek |
---|---|
Other Authors: | School of Biological Sciences |
Format: | Article |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/90274 http://hdl.handle.net/10220/48497 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge
by: Ding, Pingjian, et al.
Published: (2020) -
AtRsgA from Arabidopsis thaliana is important for maturation of the small subunit of the chloroplast ribosome
by: Janowski, Marcin, et al.
Published: (2020) -
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
by: YANG, Xinli, et al.
Published: (2017) -
QSAR classification of metabolic activation of chemicals into covalently reactive species
by: Liew, C.Y., et al.
Published: (2014) -
Visible light based occupancy inference using ensemble learning
by: Hao, Jie, et al.
Published: (2018)