Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springernature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.
Açıklama
Anahtar Kelimeler
Chemotherapy, Cancer, Classification, Proteins, Alopecia, Success, Package, Damage, Tool
Kaynak
Pharmacogenomics Journal
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
21
Sayı
6
