Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

Duan, Xiaoran; Yang, Yongli; Tan, Shengkui; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

doi:10.1007/s11517-016-1585-7

Cited by 28 publications

(23 citation statements)

References 34 publications

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“…This method is also widely used in biology studies and has achieved high performance. In 2017, it was shown that ANN could be used for the diagnosis of lung cancer (62). Meanwhile, in this study, the positive training set is small, and recent researches have revealed that ANN may improve performance for problems with small training set sizes and give better performance, especially for problems with time-series data category (63).…”

Section: Classi Er Model Selectionmentioning

confidence: 74%

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: Classi Er Model Selectionmentioning

confidence: 74%

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

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This method is also widely used in biology studies and has achieved high performance. In 2017, it was shown that ANN could be used for the diagnosis of lung cancer (65). Meanwhile, in this study, the positive training set is small, and recent researches have revealed that ANN may improve performance for problems with small training set sizes and give better performance, especially for problems with time-series data category (66).…”

Section: Classi Er Model Selectionmentioning

confidence: 74%

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

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

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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“…However, several studies have used ANNs to detect lung cancer without performing exhaled-breath analysis. These studies mainly focused on clinical parameters and biomarkers based on blood and genetic abnormalities [32,33]. As described in our training study, the optimal cut-off point chosen determines the number of false-positive cases versus the number of missed cases concerning lung cancer [13].…”

Section: Discussionmentioning

confidence: 99%

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

et al. 2020

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IntroductionExhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis.MethodsBased on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis.ResultsNSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89).ConclusionsAdding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

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Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

Cited by 28 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

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

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