Toxicity prediction using locality-sensitive deep learner

Xiu Huan Yap; Michael Raymer

doi:10.1016/j.comtox.2021.100210

Toxicity prediction using locality-sensitive deep learner

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	100210
Journal	Computational Toxicology
Volume	21
DOIs	https://doi.org/10.1016/j.comtox.2021.100210
State	Published - Feb 2022
Externally published	Yes

ASJC Scopus Subject Areas

Toxicology
Computer Science Applications
Health, Toxicology and Mutagenesis

Keywords

Attention
Deep learning
In silico toxicity prediction
Locally-linear data

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10.1016/j.comtox.2021.100210

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title = "Toxicity prediction using locality-sensitive deep learner",

keywords = "Attention, Deep learning, In silico toxicity prediction, Locally-linear data",

author = "Yap, \{Xiu Huan\} and Michael Raymer",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2022",

month = feb,

doi = "10.1016/j.comtox.2021.100210",

language = "English",

volume = "21",

journal = "Computational Toxicology",

issn = "2468-1113",

}

TY - JOUR

T1 - Toxicity prediction using locality-sensitive deep learner

AU - Yap, Xiu Huan

AU - Raymer, Michael

PY - 2022/2

Y1 - 2022/2

KW - Attention

KW - Deep learning

KW - In silico toxicity prediction

KW - Locally-linear data

UR - https://www.scopus.com/pages/publications/85121916100

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DO - 10.1016/j.comtox.2021.100210

M3 - Article

AN - SCOPUS:85121916100

SN - 2468-1113

VL - 21

JO - Computational Toxicology

JF - Computational Toxicology

M1 - 100210

ER -

Toxicity prediction using locality-sensitive deep learner

ASJC Scopus Subject Areas

Keywords

Access to Document

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