國立虎尾科技大學 |

In Silico Toxicology : = Application of Machine Learning for Predicting Toxicity of Organic Compounds.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	In Silico Toxicology :/
其他題名:	Application of Machine Learning for Predicting Toxicity of Organic Compounds.
作者:	Daghighi, Amirreza.
面頁冊數:	1 online resource (70 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Bioinformatics. -
電子資源:	click for full text (PQDT)
ISBN:	9798379698607

In Silico Toxicology : = Application of Machine Learning for Predicting Toxicity of Organic Compounds.
Daghighi, Amirreza.

In Silico Toxicology :Application of Machine Learning for Predicting Toxicity of Organic Compounds. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--North Dakota State University, 2023.

Includes bibliographical references

Understanding the toxicity of organic compounds is essential to protect human health, the environment, and ensure the safe use of chemicals. While experimental approaches are time-consuming and costly, computational studies offer cost-effective and time-efficient to predict the toxicity of organic compounds. Moreover, computational studies can reduce the need for animal testing and provide insights into the underlying mechanisms of toxicity. This thesis aims to develop Quantitative Structure-Toxicity Relationship (QSTR) models using different Machine Learning (ML) methods to predict the toxicity of organic compounds. The first study uses ensemble learning and Support Vector Regression (SVR) to estimate the toxicity of nitroaromatic compounds. The second study employs one of the largest available toxicology datasets to build a QSTR model that predicts the toxicity of various organic compounds under different experimental conditions. The proposed computational workflow can be an important milestone in developing QSTR models and paves the way for future toxicology studies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379698607Subjects--Topical Terms:

583857
Bioinformatics.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

In Silico Toxicology : = Application of Machine Learning for Predicting Toxicity of Organic Compounds.
LDR:02478ntm a22004097 4500 001 1149309
005 20241015112509.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379698607
035 $a (MiAaPQ)AAI30419097
035 $a AAI30419097
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Daghighi, Amirreza. $3 1475491
245 1 0 $a In Silico Toxicology : $b Application of Machine Learning for Predicting Toxicity of Organic Compounds.
264 0 $c 2023
300 $a 1 online resource (70 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Rasulev, Bakhtiyor.
502 $a Thesis (M.S.)--North Dakota State University, 2023.
504 $a Includes bibliographical references
520 $a Understanding the toxicity of organic compounds is essential to protect human health, the environment, and ensure the safe use of chemicals. While experimental approaches are time-consuming and costly, computational studies offer cost-effective and time-efficient to predict the toxicity of organic compounds. Moreover, computational studies can reduce the need for animal testing and provide insights into the underlying mechanisms of toxicity. This thesis aims to develop Quantitative Structure-Toxicity Relationship (QSTR) models using different Machine Learning (ML) methods to predict the toxicity of organic compounds. The first study uses ensemble learning and Support Vector Regression (SVR) to estimate the toxicity of nitroaromatic compounds. The second study employs one of the largest available toxicology datasets to build a QSTR model that predicts the toxicity of various organic compounds under different experimental conditions. The proposed computational workflow can be an important milestone in developing QSTR models and paves the way for future toxicology studies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Bioinformatics. $3 583857
650 4 $a Toxicology. $3 580973
650 4 $a Biomedical engineering. $3 588770
653 $a Machine learning
653 $a Nitroaromatic compounds
653 $a Organic compounds
653 $a QSAR
653 $a Toxicity
655 7 $a Electronic books. $2 local $3 554714
690 $a 0541
690 $a 0800
690 $a 0715
690 $a 0383
710 2 $a North Dakota State University. $b Biomedical Engineering. $3 1475492
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 84-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419097 $z click for full text (PQDT)