A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

Mirsadeghi, Leila; Banaei-Moghaddam, Ali Mohammad; Beh-Afarin, Seyed Reza; Hosseini, Reihaneh; Kavousi, Kaveh

doi:10.21203/rs.2.18222/v1

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Since then, many ensemble classi cation methods have been applied to breast cancer prognosis (9). In this regard, we reviewed 42 ensemble methods related to 18 cancers (10). Among these, 22 approaches have been reported for analyzing breast cancer data in the literature (Table 1).…”

Section: Introductionmentioning

confidence: 99%

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

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

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BackgroundToday, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.MethodsIn this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). ResultsThis study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions based on gene set enrichment analysis are discussed. Third, statistical validation and comparison of all learning methods based on evaluation metrics are done. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR<0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA, including HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reached 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.ConclusionsThis research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing.

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Section: Introductionmentioning

confidence: 99%

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

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since then, many ensemble classi cation methods have been applied to breast cancer prognosis (12). In this regard, we reviewed 42 ensemble methods related to 18 cancers (13). Among these, 22 approaches have been reported for analyzing breast cancer data in the literature (Table 1).…”

Section: Introductionmentioning

confidence: 99%

EARN: An Ensemble Machine Learning Algorithm to Predict Driver Genes in Metastatic Breast Cancer

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.Methods In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI.Results This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR<0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.Conclusions This research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphical Abstract (Figure 1).

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A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

Cited by 2 publications

References 34 publications

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

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

EARN: An Ensemble Machine Learning Algorithm to Predict Driver Genes in Metastatic Breast Cancer

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