Neural network based prediction of less side effect causing cancer drug targets in the network of MAPK pathways
| Autor: | V. M. Chandrasekaran, Sundaramurthy Pandurangan, V.K. Md Aksam |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Bioinformatics Research and Applications. 17:69 |
| ISSN: | 1744-5493 1744-5485 |
| DOI: | 10.1504/ijbra.2021.113963 |
| Popis: | Computational side-effect prediction tools assist in rational drug design to decrease the late-stage failure of the drugs. Irrational selection of cancer drug targets in the deregulated MAPK pathways causes side effects. Network centralities and biological features - Degree, Radiality, Eccentricity, Closeness, Bridging, Stress, Pagerank centralities, essentiality, pathway-specific proteins, disease-causing proteins, protein domains are exploited quantitatively. We train an artificial neural network (ANN) with 15 selected features for the binary classification of side effects causing and less side-effect causing drug targets among the non-targeted proteins. Top ranked proteins among the Degree, Eccentricity, betweenness centralities, possessing GO-based molecular function, involved in more than one Biocarta pathways, domain content are prone to cause a number of side effects than other centralities and functional features. We predicted the following 15 less side effect causing cancer drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK, GADD45, TAB2, TAB1, ELK1 and SRF. |
| Databáze: | OpenAIRE |
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