Dataset Information

Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer.

ABSTRACT: The imbalance of human gut microbiota has been associated with colorectal cancer. In recent years, metagenomics research has provided a large amount of scientific data enabling us to study the dedicated roles of gut microbes in the onset and progression of cancer. We removed unrelated and redundant features during feature selection by mutual information. We then trained a random forest classifier on a large metagenomics dataset of colorectal cancer patients and healthy people assembled from published reports and extracted and analysed the information from the learned decision trees. We identified key microbial species associated with colorectal cancers. These microbes included Porphyromonas asaccharolytica, Peptostreptococcus stomatis, Fusobacterium, Parvimonas sp., Streptococcus vestibularis and Flavonifractor plautii. We obtained the optimal splitting abundance thresholds for these species to distinguish between healthy and colorectal cancer samples. This extracted consensus decision tree may be applied to the diagnosis of colorectal cancers.

SUBMITTER: Ai D

PROVIDER: S-EPMC6410271 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer.

Ai Dongmei D Pan Hongfei H Han Rongbao R Li Xiaoxin X Liu Gang G Xia Li C LC

Genes 20190201 2

The imbalance of human gut microbiota has been associated with colorectal cancer. In recent years, metagenomics research has provided a large amount of scientific data enabling us to study the dedicated roles of gut microbes in the onset and progression of cancer. We removed unrelated and redundant features during feature selection by mutual information. We then trained a random forest classifier on a large metagenomics dataset of colorectal cancer patients and healthy people assembled from publ ...[more]

PMID: 30717284

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Project description:Background and objectives Sleep disorders related to Parkinson’s disease (PD) have recently attracted increasing attention, but there are few clinical reports on the correlation of Parkinson’s disease patients with rapid eye movement (REM) sleep behavior disorder (RBD). Therefore, this study conducted a cognitive function examination for Parkinson’s disease patients and discussed the application effect of three algorithms in the screening of influencing factors and risk prediction effects. Methods Three algorithms (logistic regression, machine learning-based regression trees and random forest) were used to establish a prediction model for PD-RBD patients, and the application effects of the three algorithms in the screening of influencing factors and the risk prediction of PD-RBD were discussed. Results The subjects included 169 patients with Parkinson’s disease (Parkinson’s disease with RBD [PD-RBD] = 69 subjects; Parkinson’s disease without RBD [PD-nRBD] = 100 subjects). This study compared the predictive performance of RF, decision tree and logistic regression, selected a final model with the best model performance and proposed the importance of variables in the final model. After the analysis, the accuracy of RF (83.05%) was better than that of the other models (decision tree = 75.10%, logistic regression = 71.62%). PQSI, Scopa-AUT score, MoCA score, MMSE score, AGE, LEDD, PD-course, UPDRS total score, ESS score, NMSQ, disease type, RLSRS, HAMD, UPDRS III and PDOnsetage are the main variables for predicting RBD, along with increased weight. Among them, PQSI is the most important factor. The prediction model of Parkinson’s disease RBD that was established in this study will help in screening out predictive factors and in providing a reference for the prognosis and preventive treatment of PD-RBD patients. Conclusions The random forest model had good performance in the prediction and evaluation of PD-RBD influencing factors and was superior to decision tree and traditional logistic regression models in many aspects, which can provide a reference for the prognosis and preventive treatment of PD-RBD patients.

Project description:ImportanceUnderstanding patterns of sun-protective behaviors and their association with sunburn can provide important insight into measurement approaches and intervention targets.ObjectiveTo assess whether decision-based modeling can be used to identify patterns of sun-protective behaviors associated with the likelihood of sunburn and to compare the predictive value of this method with traditional (ie, composite score) measurement approaches.Design, setting, and participantsThis cross-sectional study used a nationally representative sample of 31?162 US adults from the 2015 National Health Interview Survey, consisting of household interviews conducted in person and completed by telephone when necessary. Participants included civilian noninstitutionalized US adults. Data were collected from January 1 through December 31, 2015.Main outcomes and measuresThe associations among sun sensitivity, multiple sun-protective behaviors (ie, using sunscreen, seeking shade, wearing a hat, and wearing protective clothing), and sunburn were examined using a ?2 automatic interaction detection method for decision tree analysis. Results were compared with a composite score approach.ResultsIn our study population of 28?558 respondents with complete data (54.1% women; mean [SD] age, 49.0 [18.0] years), 20 patterns of sun protection were identified. Among 15?992 sun-sensitive individuals, those who used only sunscreen had the highest likelihood of sunburn (62.4%). The group with the lowest likelihood of sunburn did not report using sunscreen but engaged in the other 3 protective behaviors (24.3% likelihood of sunburn). Among 12?566 non-sun-sensitive individuals, those who engaged in all 4 protective behaviors had the lowest likelihood of sunburn (6.6%). The highest likelihood of sunburn was among those who only reported sunscreen use (26.2%). The decision tree model and the composite score approach correctly classified a similar number of cases; however, the decision tree model was superior in classifying cases with sunburn (44.3% correctly classified in the decision tree vs 25.9% with the composite score).Conclusions and relevanceThis innovative application of a decision tree analytic approach demonstrates the interactive and sometimes counterintuitive effects of multiple sun-protective behaviors on likelihood of sunburn. These data show where traditional measurement approaches of behavior may fall short and highlight the importance of linking behavior to a clinically relevant outcome. Given the scope of those affected and enormous associated health care costs, improving efforts in skin cancer prevention has the potential for a significant effect on public health.

Project description:While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.

Dataset Information

Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer.

Publications

Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer.

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