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The Discovery of New Drug-Target Interactions for Breast Cancer Treatment

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a...

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Autores principales: Song, Jiali, Xu, Zhenyi, Cao, Lei, Wang, Meng, Hou, Yan, Li, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704452/
https://www.ncbi.nlm.nih.gov/pubmed/34946556
http://dx.doi.org/10.3390/molecules26247474
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author Song, Jiali
Xu, Zhenyi
Cao, Lei
Wang, Meng
Hou, Yan
Li, Kang
author_facet Song, Jiali
Xu, Zhenyi
Cao, Lei
Wang, Meng
Hou, Yan
Li, Kang
author_sort Song, Jiali
collection PubMed
description Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.
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spelling pubmed-87044522021-12-25 The Discovery of New Drug-Target Interactions for Breast Cancer Treatment Song, Jiali Xu, Zhenyi Cao, Lei Wang, Meng Hou, Yan Li, Kang Molecules Article Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method. MDPI 2021-12-10 /pmc/articles/PMC8704452/ /pubmed/34946556 http://dx.doi.org/10.3390/molecules26247474 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Jiali
Xu, Zhenyi
Cao, Lei
Wang, Meng
Hou, Yan
Li, Kang
The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title_full The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title_fullStr The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title_full_unstemmed The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title_short The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
title_sort discovery of new drug-target interactions for breast cancer treatment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704452/
https://www.ncbi.nlm.nih.gov/pubmed/34946556
http://dx.doi.org/10.3390/molecules26247474
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