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

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 Y-K, Hwang E, Cho S-B, Pareigis S, Kyandoghere K, Ha Y-G, Kim J, Morishima A, Wagner C, Kwon H-Y, Moon Y-S, 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).

@inproceedings{ff4dfbfb84254ffa8918ba34234a9744,

title = "A denoised embedding space of genetic perturbation using Deep Metric Learning",

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",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 ; Conference date: 17-01-2022 Through 20-01-2022",

year = "2022",

doi = "10.1109/BigComp54360.2022.00085",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "378--381",

editor = "Herwig Unger and Young-Kuk Kim and Eenjun Hwang and Sung-Bae Cho and Stephan Pareigis and Kyamakya Kyandoghere and Young-Guk Ha and Jinho Kim and Atsuyuki Morishima and Christian Wagner and Hyuk-Yoon Kwon and Yang-Sae Moon and Carson Leung",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022",

}

TY - GEN

T1 - A denoised embedding space of genetic perturbation using Deep Metric Learning

AU - Ju, Minjae

AU - Lee, Sanghoon

AU - Kang, Jaewoo

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - data denoising

KW - deep metric learning

KW - drug discovery

KW - gene expression profile

UR - http://www.scopus.com/inward/record.url?scp=85127594597&partnerID=8YFLogxK

U2 - 10.1109/BigComp54360.2022.00085

DO - 10.1109/BigComp54360.2022.00085

M3 - Conference contribution

AN - SCOPUS:85127594597

T3 - Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022

SP - 378

EP - 381

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

A2 - Unger, Herwig

A2 - Kim, Young-Kuk

A2 - Hwang, Eenjun

A2 - Cho, Sung-Bae

A2 - Pareigis, Stephan

A2 - Kyandoghere, Kyamakya

A2 - Ha, Young-Guk

A2 - Kim, Jinho

A2 - Morishima, Atsuyuki

A2 - Wagner, Christian

A2 - Kwon, Hyuk-Yoon

A2 - Moon, Yang-Sae

A2 - Leung, Carson

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022

Y2 - 17 January 2022 through 20 January 2022

ER -

A denoised embedding space of genetic perturbation using Deep Metric Learning

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this