A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer

Tabl, Ashraf Abou; Alkhateeb, Abedalrhman; ElMaraghy, Waguih; Rueda, Luis; Ngom, Alioune

doi:10.3389/fgene.2019.00256

Cited by 101 publications

(57 citation statements)

References 27 publications

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“…ML algorithms are currently used for building predictive models for the classification of biological data, and identify biomarkers through a training procedure [20,21]. This technology was applied to identify marker genes in breast cancer [22], and to analyze clinical data for predicting cardiovascular and diabetes risk [23,24]. Recently, ML algorithms have been used to identify biomarkers analyzing shotgun and 16S rRNA data [25].…”

Section: Introductionmentioning

confidence: 99%

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

et al. 2020

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The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

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Section: Introductionmentioning

confidence: 99%

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

et al. 2020

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“…In recent years, the development of machine learning algorithms and the availability of gene expression data in public databases provide approaches to infer biomarkers for disease diagnosis or prognosis in a wide range of fields [30][31][32][33]. In the field of PCOS, some attempts have been made to explore a better way for PCOS diagnosis by using various machine learning algorithms [34][35][36][37][38], among which, suitable algorithms using some clinical data, such as survey data [35] or pelvic ultrasound data, were used [37].…”

Section: Discussionmentioning

confidence: 99%

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Xie

Wang

Zhou

et al. 2020

BioMed Research International

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Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.

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“…Features measured by sequencing (e.g., polymorphisms, mutations or gene expression values) are far more numerous than the cohorts of individual patients with traced clinical outcomes. For generating statistically significant predictions, extensive reduction of a pool of features under consideration is needed to make their number comparable with the number of individual samples available [10,[29][30][31]. To leverage the performance of ML in biomedicine, we recently developed an approach called flexible data trimming (Data trimming (DT) is the process of removing or excluding extreme values, or outliers, from a dataset [32]) [8,29,[33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

Tkachev

Sorokin

Borisov

et al. 2020

IJMS

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(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

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A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer

Cited by 101 publications

References 27 publications

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

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