Predicting Response to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning

Mucaki, Eliseos J.; Zhao, Jonathan Z.L.; Lizotte, Dan; Rogan, Peter K.

doi:10.1101/231712

Cited by 2 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast with heuristic approaches like differential expression, we only consider genes with evidence of a relationship to radiation response, which significantly limits the number of model features. Biochemically-inspired genomic machine learning (ML) has been used to derive high performing gene signatures that predict chemotherapy and hormone therapy responses 18 – 20 . From an initial set of mRMR-derived biochemically relevant genes, wrapper approaches for feature selection 21 are used to find an optimal set of genes that predict exposure to radiation.…”

Section: Introductionmentioning

confidence: 99%

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Zhao

Mucaki

2018

F1000Res

Self Cite

View full text Add to dashboard Cite

Gene signatures derived from transcriptomic data using machine Background: learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches.Gene Expression Omnibus (GEO) datasets of exposed human and Methods: murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets.The best human signatures we derived exhibit k-fold , and ) when validated over 85 samples. Some human ENO1 PPM1D signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures.Gene signatures for ionizing radiation exposure derived by Conclusions: 2018, 7:233 Last updated: 20 MAR 2019 Gene signatures for ionizing radiation exposure derived by Conclusions: machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Zhao

Mucaki

2018

F1000Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…In contrast with heuristic approaches like differential expression, we only consider genes with evidence of a relationship to radiation response, which significantly limits the number of model features. Biochemically-inspired genomic machine learning (ML) has been used to derive high performing gene signatures that predict chemotherapy and hormone therapy responses 13– 15 . From an initial set of mRMR-derived biochemically relevant genes, wrapper approaches for feature selection 16 are used to find an optimal set of genes that predict exposure to radiation.…”

Section: Introductionmentioning

confidence: 99%

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

2018

Self Cite

View full text Add to dashboard Cite

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

show abstract

Predicting Response to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning

Cited by 2 publications

References 52 publications

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

Contact Info

Product

Resources

About