DTIP : A Comparative Analytical Framework for Chemogenomic Drugtarget Interactions Prediction
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: Prediction of drug-target interactions is an essential step in drug discovery. Given drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and costly experimental process of drug-target interaction prediction. A large number of computational methods for solving this problem have been proposed in recent years.
OBJECTIVE: In recent years, several review articles have been published in the field of drug-target interactions prediction. Compared to other review articles, this paper includes a qualitative analysis in the form of a framework, a drug-target interactions prediction (DTIP) framework.
METHODS: The framework consists of three sections. Initially, a classification has been presented for drug-target interactions prediction methods based on the link prediction approaches used in these methods. Secondly, general evaluation criteria have been introduced for analyzing approaches. Finally, a qualitative comparison is made between each approach in terms of their advantages and disadvantages.
RESULTS: By providing a new classification of the drug-target interactions prediction approaches and comparing them with the proposed evaluation criteria, this framework provides a convenient and efficient way to select and compare the methods. Moreover, using the framework, we can improve these techniques further.
CONCLUSION: This paper provides a study to select, compare, and improve chemogenomic drugtarget interactions prediction methods. To this aim, an analytical framework is presented.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Current computer-aided drug design - 17(2021), 1 vom: 01., Seite 2-21 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Haddadi, Faraneh [VerfasserIn] |
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| Links: |
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| Themen: |
Chemogenomic |
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| Anmerkungen: |
Date Completed 21.10.2021 Date Revised 21.10.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/1573409916666191218124520 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM30455006X |
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| 500 | |a Date Revised 21.10.2021 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: Prediction of drug-target interactions is an essential step in drug discovery. Given drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and costly experimental process of drug-target interaction prediction. A large number of computational methods for solving this problem have been proposed in recent years | ||
| 520 | |a OBJECTIVE: In recent years, several review articles have been published in the field of drug-target interactions prediction. Compared to other review articles, this paper includes a qualitative analysis in the form of a framework, a drug-target interactions prediction (DTIP) framework | ||
| 520 | |a METHODS: The framework consists of three sections. Initially, a classification has been presented for drug-target interactions prediction methods based on the link prediction approaches used in these methods. Secondly, general evaluation criteria have been introduced for analyzing approaches. Finally, a qualitative comparison is made between each approach in terms of their advantages and disadvantages | ||
| 520 | |a RESULTS: By providing a new classification of the drug-target interactions prediction approaches and comparing them with the proposed evaluation criteria, this framework provides a convenient and efficient way to select and compare the methods. Moreover, using the framework, we can improve these techniques further | ||
| 520 | |a CONCLUSION: This paper provides a study to select, compare, and improve chemogenomic drugtarget interactions prediction methods. To this aim, an analytical framework is presented | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Chemogenomic | |
| 650 | 4 | |a comparative analytical framework | |
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| 650 | 4 | |a drug-target interactions network | |
| 650 | 4 | |a drug-target interactions prediction | |
| 650 | 4 | |a link prediction | |
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| 700 | 1 | |a Kayvanpour, Mohammad Reza |e verfasserin |4 aut | |
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