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|Title:||Chemical Induced Differential Gene Expression Prediction on LINCS Database||Authors:||Işık, R.
Maral, B. C.
|Keywords:||IEEE International Conference on Bioinformatics and BioEngineering||Issue Date:||Sep-2020||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Source:||Işık, R., Ekşioğlu, I., Maral, B. C., Bardak, B., & Tan, M. (2020, October). Chemical Induced Differential Gene Expression Prediction on LINCS Database. In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 111-114). IEEE.||Abstract:||Understanding the mechanism of action for drugs is vital for drug discovery. Identifying the effect of drugs on gene expression can shed light on the system-side influence of the chemical compounds in biological organisms. In this paper, we propose to use multi-task neural networks to predict chemical induced differential gene expression on cancer cell lines based solely on features of chemicals. Our model predicts differential gene expression identified by a method called Characteristic Direction on a large scale chemical induced gene expression database (LINCS L1000). The results show that the multi-task networks outperform the other single task baselines. We also compare different representations of chemicals and report effect of clustering genes on the prediction performance. © 2020 IEEE.||URI:||https://hdl.handle.net/20.500.11851/4259
|Appears in Collections:||Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection|
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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checked on Dec 26, 2022
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