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Title: Chemical Induced Differential Gene Expression Prediction on LINCS Database
Authors: Işık, R.
Ekşioğlu, I.
Maral, B. C.
Bardak, B.
Tan, Mehmet
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.
ISBN: 978-172819574-2
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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