A denoised embedding space of genetic perturbation using Deep Metric Learning

Minjae Ju; Sanghoon Lee; Jaewoo Kang

doi:10.1109/BigComp54360.2022.00085

A denoised embedding space of genetic perturbation using Deep Metric Learning

Minjae Ju, Sanghoon Lee, Jaewoo Kang

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Identifying and relieving internal noises of expression profile is crucial in drug discovery. Among various perturbagens, the most common cause of off-target effects in genetic perturbation is known as seed effects. In this paper, we propose a model to denoise seed effects in LINCS/L1000 gene knock down (KD) dataset by using deep metric learning. Results show that our model can embed profiles with the identical gene target into similar embedding spaces, whereas profiles with the same seed sequence but with different gene targets can embed farther away. This robust embedding space could help reveal the mechanism of actions (MoA) of compounds or solve other downstream tasks using expression profiles.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Editors	Herwig Unger, Young-Kuk Kim, Eenjun Hwang, Sung-Bae Cho, Stephan Pareigis, Kyamakya Kyandoghere, Young-Guk Ha, Jinho Kim, Atsuyuki Morishima, Christian Wagner, Hyuk-Yoon Kwon, Yang-Sae Moon, Carson Leung
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	378-381
Number of pages	4
ISBN (Electronic)	9781665421973
DOIs	https://doi.org/10.1109/BigComp54360.2022.00085
Publication status	Published - 2022
Event	2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 - Daegu, Korea, Republic of Duration: 2022 Jan 17 → 2022 Jan 20

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022

Conference

Conference	2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Country/Territory	Korea, Republic of
City	Daegu
Period	22/1/17 → 22/1/20

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

data denoising
deep metric learning
drug discovery
gene expression profile

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management
Health Informatics

Access to Document

10.1109/BigComp54360.2022.00085

Cite this

Ju, M., Lee, S., & Kang, J. (2022). A denoised embedding space of genetic perturbation using Deep Metric Learning. In H. Unger, Y.-K. Kim, E. Hwang, S.-B. Cho, S. Pareigis, K. Kyandoghere, Y.-G. Ha, J. Kim, A. Morishima, C. Wagner, H.-Y. Kwon, Y.-S. Moon, & C. Leung (Eds.), Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 (pp. 378-381). (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp54360.2022.00085

A denoised embedding space of genetic perturbation using Deep Metric Learning. / Ju, Minjae; Lee, Sanghoon; Kang, Jaewoo.
Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. ed. / Herwig Unger; Young-Kuk Kim; Eenjun Hwang; Sung-Bae Cho; Stephan Pareigis; Kyamakya Kyandoghere; Young-Guk Ha; Jinho Kim; Atsuyuki Morishima; Christian Wagner; Hyuk-Yoon Kwon; Yang-Sae Moon; Carson Leung. Institute of Electrical and Electronics Engineers Inc., 2022. p. 378-381 (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ju, M, Lee, S & Kang, J 2022, A denoised embedding space of genetic perturbation using Deep Metric Learning. in H Unger, Y-K Kim, E Hwang, S-B Cho, S Pareigis, K Kyandoghere, Y-G Ha, J Kim, A Morishima, C Wagner, H-Y Kwon, Y-S Moon & C Leung (eds), Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, Institute of Electrical and Electronics Engineers Inc., pp. 378-381, 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, Daegu, Korea, Republic of, 22/1/17. https://doi.org/10.1109/BigComp54360.2022.00085

Ju M, Lee S, Kang J. A denoised embedding space of genetic perturbation using Deep Metric Learning. In Unger H, Kim YK, Hwang E, Cho SB, Pareigis S, Kyandoghere K, Ha YG, Kim J, Morishima A, Wagner C, Kwon HY, Moon YS, Leung C, editors, Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 378-381. (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022). doi: 10.1109/BigComp54360.2022.00085

Ju, Minjae ; Lee, Sanghoon ; Kang, Jaewoo. / A denoised embedding space of genetic perturbation using Deep Metric Learning. Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. editor / Herwig Unger ; Young-Kuk Kim ; Eenjun Hwang ; Sung-Bae Cho ; Stephan Pareigis ; Kyamakya Kyandoghere ; Young-Guk Ha ; Jinho Kim ; Atsuyuki Morishima ; Christian Wagner ; Hyuk-Yoon Kwon ; Yang-Sae Moon ; Carson Leung. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 378-381 (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022).

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abstract = "Identifying and relieving internal noises of expression profile is crucial in drug discovery. Among various perturbagens, the most common cause of off-target effects in genetic perturbation is known as seed effects. In this paper, we propose a model to denoise seed effects in LINCS/L1000 gene knock down (KD) dataset by using deep metric learning. Results show that our model can embed profiles with the identical gene target into similar embedding spaces, whereas profiles with the same seed sequence but with different gene targets can embed farther away. This robust embedding space could help reveal the mechanism of actions (MoA) of compounds or solve other downstream tasks using expression profiles. ",

keywords = "data denoising, deep metric learning, drug discovery, gene expression profile",

author = "Minjae Ju and Sanghoon Lee and Jaewoo Kang",

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A denoised embedding space of genetic perturbation using Deep Metric Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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