Dataset Information

Prediction of the metabolic syndrome status based on dietary and genetic parameters, using Random Forest.

ABSTRACT: Metabolic syndrome (MS) is a cluster of metabolic abnormalities associated with an increased risk of developing cardio-vascular diseases, stroke or type II diabetes. Overall, the aetiology of MS is complex and is determined by the interplay between genetic and environmental factors although it is still difficult to untangle their respective roles. The aim of this study was to determine which factors and/or combination of factors could be predictive of MS status. Using a large case-control study nested in a well-characterized cohort, we investigated genetic and dietary factors collected at entry in subjects having developed MS 7 years later. We used a classification technique called Random Forest to predict the MS status from the analysis of these data. We obtained an overall out-of-bag estimation of the correct classification rate of 71.7% (72.1% for the control subjects and 70.7% for the cases). The plasma concentration of 16.1 was the most discriminative variable, followed by plasma concentration of C18.3(n-6) and C18.2. Three SNPs were selected by Random Forest (APOB rs512535, LTA rs915654 and ACACB rs4766587). These SNPs were also significantly associated to the MS by a univariate Fisher test.

SUBMITTER: Szabo de Edelenyi F

PROVIDER: S-EPMC2593021 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Prediction of the metabolic syndrome status based on dietary and genetic parameters, using Random Forest.

Szabo de Edelenyi Fabien F Goumidi Louisa L Bertrais Sandrine S Phillips Catherine C Macmanus Ross R Roche Helen H Planells Richard R Lairon Denis D

Genes & nutrition 20081126 3-4

Metabolic syndrome (MS) is a cluster of metabolic abnormalities associated with an increased risk of developing cardio-vascular diseases, stroke or type II diabetes. Overall, the aetiology of MS is complex and is determined by the interplay between genetic and environmental factors although it is still difficult to untangle their respective roles. The aim of this study was to determine which factors and/or combination of factors could be predictive of MS status. Using a large case-control study ...[more]

PMID: 19034549

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Project description:BackgroundToll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling.ResultsUsing in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including 'CC', 'GG','AG', 'CCCG' and 'CGGC' were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity.ConclusionWe combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

Project description:BackgroundMicrosatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size.ResultsWe developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases).ConclusionThese results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.

Dataset Information

Prediction of the metabolic syndrome status based on dietary and genetic parameters, using Random Forest.

Publications

Prediction of the metabolic syndrome status based on dietary and genetic parameters, using Random Forest.

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