Interaction-Based Feature Selection Algorithm Outperforms Polygenic Risk Score in Predicting Parkinson’s Disease Status
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SUBMITTER: Cope J
PROVIDER: S-EPMC8564367 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The extent of weight loss among patients undergoing bariatric surgery is highly variable. Herein, we tested the contribution of genetic background to such interindividual variability after biliopancreatic diversion with duodenal switch. METHODS:Percentage of excess body weight loss (%EBWL) was monitored in 865 patients over a period of 48 months after bariatric surgery, and two polygenic risk scores were constructed with 186 and 11 (PRS186 and PRS11) single nucleotide polymorphisms previously associated with body mass index (BMI). RESULTS:The accuracy of the %EBWL logistic prediction model - including initial BMI, age, sex, and surgery modality, and assessed as the area under the receiver operating characteristics (ROC) curve adjusted for optimism (AUCadj = 0.867) - significantly increased after the inclusion of PRS186 (?AUCadj = 0.021; 95% CI of the difference [95% CIdiff] = 0.005-0.038) but not PRS11 (?AUCadj= 0.008; 95% CIdiff= -0.003-0.019). The overall fit of the longitudinal linear mixed model for %EBWL showed a significant increase after addition of PRS186 (-2 log-likelihood = 12.3; P = 0.002) and PRS11 (-2 log-likelihood = 9.9; P = 0.007). A significant interaction with postsurgery time was found for PRS186 (? = -0.003; P = 0.008) and PRS11 (? = -0.008; P = 0.03). The inclusion of PRS186 and PRS11 in the model improved the cost-effectiveness of bariatric surgery by reducing the percentage of false negatives from 20.4% to 10.9% and 10.2%, respectively. CONCLUSION:These results revealed that genetic background has a significant impact on weight loss after biliopancreatic diversion with duodenal switch. Likewise, the improvement in weight loss prediction after addition of polygenic risk scores is cost-effective, suggesting that genetic testing could potentially be used in the presurgical assessment of patients with severe obesity. FUNDING:Heart and Stroke Foundation of Canada (G-17-0016627) and Canada Research Chair in Genomics Applied to Nutrition and Metabolic Health (no. 950-231-580).
| S-EPMC6171810 | biostudies-other
Project description:Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.
| S-EPMC8218915 | biostudies-literature
Project description:The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of "large p small n" into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.
| S-EPMC5638869 | biostudies-literature
Project description:BACKGROUND: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. RESULTS: In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. CONCLUSIONS: We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows.
| S-EPMC2989984 | biostudies-literature
Project description:Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.
| S-EPMC9322764 | biostudies-literature
Project description:Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.
| S-EPMC7886179 | biostudies-literature
Project description:ObjectiveTo examine functional status versus medical comorbidities as predictors of acute care readmissions in medically complex patients.DesignRetrospective database study.SettingU.S. inpatient rehabilitation facilities.ParticipantsSubjects included 120,957 patients in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011.InterventionsA Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Functional status was measured by the FIM(®) motor score. The Basic Model was compared to six other predictive models-three Basic Plus Models that added a comorbidity measure to the Basic Model and three Gender-Comorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance.Main outcome measuresWe investigated 3-, 7-, and 30-day readmission to acute care hospitals from inpatient rehabilitation facilities.ResultsBasic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively. The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model, and the best-performing Gender-Comorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model.ConclusionsReadmission models based on functional status consistently outperform models based on medical comorbidities. There is opportunity to improve current national readmission risk models to more accurately predict readmissions by incorporating functional data.
| S-EPMC4617914 | biostudies-literature
Project description:There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.Supplementary informationThe online version contains supplementary material available at 10.1007/s42979-021-00687-5.
| S-EPMC8147911 | biostudies-literature
Project description:Feature selection is an independent technology for high-dimensional datasets that has been widely applied in a variety of fields. With the vast expansion of information, such as bioinformatics data, there has been an urgent need to investigate more effective and accurate methods involving feature selection in recent decades. Here, we proposed the hybrid MMPSO method, by combining the feature ranking method and the heuristic search method, to obtain an optimal subset that can be used for higher classification accuracy. In this study, ten datasets obtained from the UCI Machine Learning Repository were analyzed to demonstrate the superiority of our method. The MMPSO algorithm outperformed other algorithms in terms of classification accuracy while utilizing the same number of features. Then we applied the method to a biological dataset containing gene expression information about liver hepatocellular carcinoma (LIHC) samples obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). On the basis of the MMPSO algorithm, we identified a 18-gene signature that performed well in distinguishing normal samples from tumours. Nine of the 18 differentially expressed genes were significantly up-regulated in LIHC tumour samples, and the area under curves (AUC) of the combination seven genes (ADRA2B, ERAP2, NPC1L1, PLVAP, POMC, PYROXD2, TRIM29) in classifying tumours with normal samples was greater than 0.99. Six genes (ADRA2B, PYROXD2, CACHD1, FKBP1B, PRKD1 and RPL7AP6) were significantly correlated with survival time. The MMPSO algorithm can be used to effectively extract features from a high-dimensional dataset, which will provide new clues for identifying biomarkers or therapeutic targets from biological data and more perspectives in tumor research.
| S-EPMC9044222 | biostudies-literature
Project description:Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes. Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to globally score all the features, and the selector adopts a dependent sub-NN to locally evaluate the representation abilities for selecting features. Further, we present algorithmic stability analysis and show that our algorithm has a performance guarantee via a generalization error bound. Extensive experimental results on real-world datasets demonstrate superior generalization performance of our proposed algorithm to strong baseline methods. Also, the properties revealed by our theoretical analysis and the stability of our algorithm-selected features are empirically confirmed.
| S-EPMC9524443 | biostudies-literature