Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data

Ma, Baoshan; Meng, Fanyu; Ge, Yan; Yan, Haowen; Chai, Bingjie; Song, Fengju

doi:10.1016/j.compbiomed.2020.103761

Cited by 151 publications

(82 citation statements)

References 46 publications

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“…Random forest is a decision tree algorithm that has shown to be one of the most effective methods for classification of microbiome data, particularly 16S rRNA sequencing data ( Statnikov et al, 2013 ). XGBoost, also a tree-based algorithm, has been recently shown to outperform other machine learning algorithms on a variety of biological datasets ( Dimitrakopoulos et al, 2018 ; Ma et al, 2020 ). Further, we included ridge regression, another widely used algorithm that differs from these tree-based models in that it is a logistic regression algorithm with L2 regularization that still enables us to compare its feature ranking to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

Volkova

Ruggles

2021

Front. Microbiol.

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Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in 12 autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression, and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found the two tree-based methods, XGBoost and random forest, most capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium, and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus, and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 215 metabolites associated with autoimmunity-predictive taxa.

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Section: Introductionmentioning

confidence: 99%

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

Volkova

Ruggles

2021

Front. Microbiol.

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“…Unclear subtype refers to patient samples that lack information on each of the three IHC markers. With the explosive growth of massive biological data, the transformation of traditional biological statistical methods to computer-aided methods makes machine learning become an important part of predicting cancer prognosis [14]. If all the features in these samples are used to classify and regress, it will lead to overfitting.…”

Section: Introductionmentioning

confidence: 99%

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Lin

Zhang

Cao

et al. 2020

Genes

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With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

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“…The main objective of this algorithm to achieved high accuracy or increase retrained the weak learners for particular data into strong learners (Table 5). Ma B. et al, (2020), introduced about Meta classifier ensemble method in data science and convert organized weak learners into strong learners. Gradient boosting works as adaptive boosting algorithms but differ in optimize problems.…”

Section: Adaboostm1 Ensemble Methodsmentioning

confidence: 99%

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Yadav

Pal

2021

International Journal of Big Data and Analytics in Healthcare

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This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

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Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data

Cited by 151 publications

References 46 publications

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

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