Dataset Information

Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

ABSTRACT: In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.

SUBMITTER: Jing R

PROVIDER: S-EPMC5818887 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

Jing Runyu R Liang Yu Y Ran Yi Y Feng Shengzhong S Wei Yanjie Y He Li L

International journal of genomics 20180110

In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in pr ...[more]

PMID: 29546047

Similar Datasets

Project description:Intrusion detection systems (IDSs) analyze internet activities and traffic to detect potential attacks, thereby safeguarding computer systems. In this study, researchers focused on developing an advanced IDS that achieves high accuracy through the application of feature selection and ensemble learning methods. The utilization of the CIC-CSE-IDS2018 dataset for training and testing purposes adds relevance to the study. The study comprised two key stages, each contributing to its significance. In the first stage, the researchers reduced the dataset through strategic feature selection and carefully selected algorithms for ensemble learning. This process optimizes the IDS's performance by selecting the most informative features and leveraging the strengths of different classifiers. In the second stage, the ensemble learning approach was implemented, resulting in a powerful model that combines the benefits of multiple algorithms. The results of the study demonstrate its impact on improving attack detection and reducing detection time. By applying techniques such as Spearman's correlation analysis, recursive feature elimination (RFE), and chi-square test methods, the researchers identified key features that enhance the IDS's performance. Furthermore, the comparison of different classifiers showcased the effectiveness of models such as extra trees, decision trees, and logistic regression. These models not only achieved high accuracy rates but also considered the practical aspect of execution time. The study's overall significance lies in its contribution to advancing IDS capabilities and improving computer security. By adopting an ensemble learning approach and carefully selecting features and classifiers, the researchers created a model that outperforms individual classifier approaches. This model, with its high accuracy rate, further validates the effectiveness of ensemble learning in enhancing IDS performance. The findings of this study have the potential to drive future developments in intrusion detection systems and have a tangible impact on ensuring robust computer security in various domains.

Project description:In this study we introduce a hybrid ensemble consisting of air quality models operating at both the global and regional scale. The work is motivated by the fact that these different types of models treat specific portions of the atmospheric spectrum with different levels of detail, and it is hypothesized that their combination can generate an ensemble that performs better than mono-scale ensembles. A detailed analysis of the hybrid ensemble is carried out in the attempt to investigate this hypothesis and determine the real benefit it produces compared to ensembles constructed from only global-scale or only regional-scale models. The study utilizes 13 regional and 7 global models participating in the Hemispheric Transport of Air Pollutants phase 2 (HTAP2)-Air Quality Model Evaluation International Initiative phase 3 (AQMEII3) activity and focuses on surface ozone concentrations over Europe for the year 2010. Observations from 405 monitoring rural stations are used for the evaluation of the ensemble performance. The analysis first compares the modelled and measured power spectra of all models and then assesses the properties of the mono-scale ensembles, particularly their level of redundancy, in order to inform the process of constructing the hybrid ensemble. This study has been conducted in the attempt to identify that the improvements obtained by the hybrid ensemble relative to the mono-scale ensembles can be attributed to its hybrid nature. The improvements are visible in a slight increase of the diversity (4 % for the hourly time series, 10 % for the daily maximum time series) and a smaller improvement of the accuracy compared to diversity. Root mean square error (RMSE) improved by 13-16 % compared to G and by 2-3 % compared to R. Probability of detection (POD) and false-alarm rate (FAR) show a remarkable improvement, with a steep increase in the largest POD values and smallest values of FAR across the concentration ranges. The results show that the optimal set is constructed from an equal number of global and regional models at only 15 % of the stations. This implies that for the majority of the cases the regional-scale set of models governs the ensemble. However given the high degree of redundancy that characterizes the regional-scale models, no further improvement could be expected in the ensemble performance by adding yet more regional models to it. Therefore the improvement obtained with the hybrid set can confidently be attributed to the different nature of the global models. The study strongly reaffirms the importance of an in-depth inspection of any ensemble of opportunity in order to extract the maximum amount of information and to have full control over the data used in the construction of the ensemble.

Project description:PurposeTo screen manuscripts that discuss rehabilitation protocols for patients who underwent superior capsular reconstruction (SCR) to elucidate whether a standard rehabilitation algorithm exists for SCR.MethodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (i.e., PRISMA) guidelines. PubMed (MEDLINE) and Embase were searched using pertinent Boolean operation terms "superior capsular reconstruction" and "rotator cuff repair rehabilitation," and articles that included rehabilitation protocols following superior capsular reconstruction surgery were reviewed. Two independent reviewers performed the search and quality assessment.ResultsA total of 549 articles were yielded after our database search. Fourteen studies fulfilled our inclusion criteria and were included in the review. Study designs included 9 editorials, 3 case series, and 2 case reports. Each study included in this review used a unique rehabilitation algorithm that posed significant variability between the protocols. Four phases were identified to summarize each protocol and were used as a basis of discussion-sling versus brace time (3-6 weeks for comfort/removal vs complete immobilization), passive range of motion (immediately after surgery to initiation at 6 weeks), active range of motion (4-8 weeks), and strengthening/return to full activity (12-52 weeks). Initiation of rehabilitation, length of time spent in each phase, types of exercises, and overarching goals for return to function were significantly variable and were decided upon by the surgeon based on current massive rotator cuff repair protocols. Presently, there is no standard rehabilitation protocol for SCR.ConclusionsSCR is a relatively new procedure that is gaining rapid popularity with promising outcomes. Based on our review, there is no standard rehabilitation protocol in place; thus, it is not possible to recommend an evidence-based rehabilitation protocol following SCR at this time.Level of evidenceLevel V, systematic review of Level IV and V studies.

Dataset Information

Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

Publications

Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets