Performance‐weighted‐voting model: An ensemble machine learning method for cancer type classification using whole‐exome sequencing mutation

Li, Yawei; Luo, Yuan

doi:10.1007/s40484-020-0226-1

Cited by 20 publications

(13 citation statements)

References 79 publications

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“…Furthermore, from their research soft voting model had the overall accuracy output comparatively lesser than that of the performance model. However, from the present research the soft-voting ensemble model performed much better as compared the performance model, due to the three classifiers that were used (as mentioned in " Weighted ensemble learning classifier " section), being able to distinguish and give better probability values as compared to the five weak classifiers used in Li et al [ 71 ]. The model designed in the present work also resulted in much larger true positives, and hence a better method for the early prediction of 5 classes of cancer as mentioned in " Data clean-up and obtaining a derived dataset " section.…”

Section: Discussionmentioning

confidence: 60%

“…In Li et al [ 71 ], the reported overall accuracy was 71.46% for the classification of 14 types of cancer class with the use of performance weighted voting ensemble on five classifiers, logistic regression, support vector machine, random forest, XGBoost and neural networks. From Table 7 , the overall weighted accuracy for 8-cancer types calculated for the five classifiers mentioned above, was well below 70% [ 71 ]. Only the performance weighted voting ensemble model resulted in an overall accuracy of 71.46 [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

“…From Table 7 , the overall weighted accuracy for 8-cancer types calculated for the five classifiers mentioned above, was well below 70% [ 71 ]. Only the performance weighted voting ensemble model resulted in an overall accuracy of 71.46 [ 71 ]. This clearly shows that the ensemble model with performance weighted voting for greater number of classifiers doesn’t yield significant results, as it is necessary to define a distinguishable structure for the exome dataset by including hyperplane distinction.…”

Section: Discussionmentioning

confidence: 99%

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Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Syed¹,

Anbalagan²,

Setlur³

et al. 2022

BMC Bioinformatics

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Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

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Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Syed¹,

Anbalagan²,

Setlur³

et al. 2022

BMC Bioinformatics

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“…In addition, we have included a feature in the software that allows for stringing together of consecutive operon pairs into multi-gene operons. Other applications in genomics where ensemble methods have proven very useful include annotation of genomic islands, detection of genomic mutations, and gene expression-based phenotype prediction [50][51][52][53]. The development of these flexible methods is critical for weathering the natural and technical variation between organisms and data sets, which we can see even between the data sets that we chose to analyze in this study.…”

Section: Discussionmentioning

confidence: 99%

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Krishnakumar¹,

Ruffing²,

Segata³

2022

PLoS Comput Biol

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Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

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“…We also provide the code required to re-train our models as data acquisition evolves and novel sequencing data types emerge, which given the statistical front-end transformation, should be broadly applicable. Other applications in genomics where ensemble methods have proven very useful include annotation of genomic islands, detection of genomic mutations, and gene expression-based phenotype prediction [44][45][46][47] . The development of these flexible methods is critical for weathering the natural and technical variation between organisms and data sets, which we can see even between the data sets that we chose to analyze in this study.…”

Section: Discussionmentioning

confidence: 99%

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Krishnakumar

Ruffing

2021

Preprint

View full text Add to dashboard Cite

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine whether the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high coverage of operons or high accuracy of the calls. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded on as additional data is generated and incorporated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility and adaptability.

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Performance‐weighted‐voting model: An ensemble machine learning method for cancer type classification using whole‐exome sequencing mutation

Cited by 20 publications

References 79 publications

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

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