EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

Mirsadeghi, Leila; Hosseini, Reihaneh; Banaei-Moghaddam, Ali Mohammad; Kavousi, Kaveh

doi:10.1186/s12920-021-00974-3

Cited by 16 publications

(4 citation statements)

References 76 publications

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“…For example, the SVM is a nonlinear algorithm, RF works with a decision tree group of algorithms, and the ANN is a neural networks-based algorithm. Various researchers have used these techniques in numerous studies [37][38][39][40]. Likewise, we had also used these techniques to develop predictive algorithms like QSPpred [25], VIR-siRNApred [41], AVP-IC50Pred [42], anti-flavi [12] and many more.…”

Section: Discussionmentioning

confidence: 99%

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Rajput

Kumar

2021

Mol Divers

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Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC 50 values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T 274 ) dataset. The robustness of the developed models was crossevaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https:// bioin fo. imtech. res. in/ manojk/ antie bola. We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

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Section: Discussionmentioning

confidence: 99%

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Rajput

Kumar

2021

Mol Divers

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“…It was previously reported that STAT5B ectopically expressed in MCF7 and T47D activates cell proliferation and anchorage-independent growth (Tang et al 2010). Another protein ERα-regulated protein from TGFα-EGFR is PLCB1 which was recently identified as a metastatic breast cancer driver gene (Mirsadeghi et al 2021). PLCB1 was also found to be overexpressed in MCF7 WWOX-silenced cells under estradiol treatment.…”

Section: Wwox and Tgfα And Tgfβmentioning

confidence: 92%

TGFα-EGFR pathway in breast carcinogenesis, association with WWOX expression and estrogen activation

et al. 2022

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WWOX is a tumor-suppressive steroid dehydrogenase, which relationship with hormone receptors was shown both in animal models and breast cancer patients. Herein, through nAnT-iCAGE high-throughput gene expression profiling, we studied the interplay of estrogen receptors and the WWOX in breast cancer cell lines (MCF7, T47D, MDA-MB-231, BT20) under estrogen stimulation and either introduction of the WWOX gene by retroviral transfection (MDA-MB-231, T47D) or silenced with shRNA (MCF7, BT20). Additionally, we evaluated the consequent biological characteristics by proliferation, apoptosis, invasion, and adhesion assays. TGFα-EGFR signaling was found to be significantly affected in all examined breast cancer cell lines in response to estrogen and strongly associated with the level of WWOX expression, especially in ER-positive MCF7 cells. Under the influence of 17β-estradiol presence, biological characteristics of the cell lines were also delineated. The study revealed modulation of adhesion, invasion, and apoptosis. The obtained results point at a complex role of the WWOX gene in the carcinogenesis of the breast tissue, which seems to be closely related to the presence of estrogen α and/or β receptors.

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“…As the volume and complexity of gene expression data burgeon, ML offers a suite of algorithms capable of deciphering intricate patterns within the data [ 12 ]. Studies have utilized various ML approaches, such as decision trees (DT) [ 13 ], neural networks (NN) [ 14 ], support vector machines (SVM) [ 15 ], logistic regression (LR) [ 16 ], and random forests (RF) [ 17 ], alongside more recent innovations in deep learning (DL) [ 18 , 19 ] and ensemble learning [ 20 , 21 ] methods such as extreme gradient boosting (XGBoost) [ 5 ] and adaptive boosting (AdaBoost) [ 22 ], to identify significant biomarkers in BC.…”

Section: Literature Reviewmentioning

confidence: 99%

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Rakhshaninejad,

Fathian,

Shirkoohi

et al. 2024

BMC Bioinformatics

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Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

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EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

Cited by 16 publications

References 76 publications

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

TGFα-EGFR pathway in breast carcinogenesis, association with WWOX expression and estrogen activation

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

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