Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME.
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ABSTRACT:
SUBMITTER: Yousef M
PROVIDER: S-EPMC7802119 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundClassification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.ResultsWe have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs), a supervised machine learning classification method, to identify and score (rank) those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE) is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA) with recursive feature elimination (SVM-RFE and PDA-RFE) are used to remove genes based on their individual discriminant weights.ConclusionSVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together provide greater insight into the structure of the microarray data. Clustering genes for classification appears to result in some concomitant clustering of samples into subgroups. Our present implementation of SVM-RCE groups genes using the correlation metric. The success of the SVM-RCE method in classification suggests that gene interaction networks or other biologically relevant metrics that group genes based on functional parameters might also be useful.
| S-EPMC1877816 | biostudies-literature
Project description:BACKGROUND: Comparison and classification of metagenome samples is one of the major tasks in the study of microbial communities of natural environments or niches on human bodies. Bioinformatics methods play important roles on this task, including 16S rRNA gene analysis and some alignment-based or alignment-free methods on metagenomic data. Alignment-free methods have the advantage of not depending on known genome annotations and therefore have high potential in studying complicated microbiomes. However, the existing alignment-free methods are all based on unsupervised learning strategy (e.g., PCA or hierarchical clustering). These types of methods are powerful in revealing major similarities and grouping relations between microbiome samples, but cannot be applied for discriminating predefined classes of interest which might not be the dominating assortment in the data. Supervised classification is needed in the latter scenario, with the goal of classifying samples into predefined classes and finding the features that can discriminate the classes. The effectiveness of supervised classification with alignment-based features on metagenomic data have been shown in some recent studies. The application of alignment-free supervised classification methods on metagenome data has not been well explored yet. RESULTS: We developed a method for this task using k-tuple frequencies as features counted directly from metagenome short reads and the R-SVM (Recursive SVM) for feature selection and classification. We tested our method on a simulation dataset, a real dataset composed of several known genomes, and a real metagenome NGS short reads dataset. Experiments on simulated data showed that the method can classify the classes almost perfectly and can recover major sequence signatures that distinguish the two classes. On the real human gut metagenome data, the method can discriminate samples of inflammatory bowel disease (IBD) patients from control samples with high accuracy, which cannot be separated when comparing the samples with unsupervised clustering approaches. CONCLUSIONS: The proposed alignment-free supervised classification method can perform well in discriminating of metagenomic samples of predefined classes and in selecting characteristic sequence features for the discrimination. This study shows as an example on the feasibility of using metagenome sequence features of microbiomes on human bodies to study specific human health conditions using supervised machine learning methods.
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Project description:Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD's. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.
| S-EPMC9878829 | biostudies-literature
Project description:There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With membership queries, one can check whether changing the value of a feature in an example changes the label. In the real-world, we usually cannot get answers to such queries, so our approach instead makes these queries to a trained (imperfect) non-linear model. Because SVMs are widely used in bioinformatics, our empirical results use a real-world cancer genomics problem; because ground truth is not known for this task, we discuss the potential insights provided. We also evaluate on synthetic data where ground truth is known.
| S-EPMC6887481 | biostudies-literature
Project description:Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.
| S-EPMC6861587 | biostudies-literature
Project description:MotivationAdvances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared to observations. Moreover, biological processes are associated with networks of core and peripheral genes, while traditional feature selection approaches capture only core genes.ResultsTo overcome these limitations, we present dRFEtools that implements dynamic recursive feature elimination (RFE), reducing computational time with high accuracy compared to standard RFE, expanding dynamic RFE to regression algorithms, and outputting the subsets of features that hold predictive power with and without peripheral features. dRFEtools integrates with scikit-learn (the popular Python machine learning platform) and thus provides new opportunities for dynamic RFE in large-scale omics data while enhancing its interpretability.Availability and implementationdRFEtools is freely available on PyPI at https://pypi.org/project/drfetools/ or on GitHub https://github.com/LieberInstitute/dRFEtools, implemented in Python 3, and supported on Linux, Windows, and Mac OS.
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Project description:Gene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible. Many of the existing gene selection algorithms suffer from either low performance or large size. In this study, we propose a wrapper gene selection approach, named WERFE, within a recursive feature elimination (RFE) framework to make the classification more efficient. This WERFE employs an ensemble strategy, takes advantages of a variety of gene selection methods and assembles the top selected genes in each approach as the final gene subset. By integrating multiple gene selection algorithms, the optimal gene subset is determined through prioritizing the more important genes selected by each gene selection method and a more discriminative and compact gene subset can be selected. Experimental results show that the proposed method can achieve state-of-the-art performance.
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Project description:Purpose:Patients with chronic obstructive pulmonary disease (COPD) would have a poor prognosis if they were not continuously managed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. We aim to develop a model to classify whether COPD patients have been continuously managed according to GOLD in the previous year. Methods:The Managed group were COPD patients from a prospective cohort from November 2017 to November 2019, who have been continuously managed according to GOLD for 1 year. The Control group were COPD patients who were not continuously managed according to GOLD. They were from a retrospective cohort from October 2016 to October 2017 in the same hospitals as the Managed group. A synthetic minority over-sampling technique (SMOTE) algorithm was used to up-sample the Managed group in a training dataset. Features for classification were selected using a support vector machine recursive feature elimination (SVM-RFE) algorithm. The classification model was developed using LibSVM, and its performance was assessed on the testing dataset. Results:The final analysis included 15 subjects in the Managed group and 191 in the Control group. SVM-RFE selects nine features including smoking history, post-bronchodilator (post-)FVC before management, and those after 1-year follow-up (BMI, moderate and severe AECOPD frequency in previous 12 months, mMRC score, post-FEV1, post-FEV1%pred, post-FVC, and post-FEV1/FVC). For our model, positive predictive value is 66.7%, F1 score is 0.978, and AUC is 0.987. Conclusion:SVM classifier combined with SVM-REF feature selection algorithm could achieve good classification between COPD patients who are or are not continuously managed. This model could be applied in clinical practice to help doctors make decisions and enhance COPD patients' compliance with standard treatment.
| S-EPMC7649211 | biostudies-literature
Project description:BACKGROUND: Breast cancer is worldwide the second most common type of cancer after lung cancer. Traditional mammography and Tissue Microarray has been studied for early cancer detection and cancer prediction. However, there is a need for more reliable diagnostic tools for early detection of breast cancer. This can be a challenge due to a number of factors and logistics. First, obtaining tissue biopsies can be difficult. Second, mammography may not detect small tumors, and is often unsatisfactory for younger women who typically have dense breast tissue. Lastly, breast cancer is not a single homogeneous disease but consists of multiple disease states, each arising from a distinct molecular mechanism and having a distinct clinical progression path which makes the disease difficult to detect and predict in early stages. RESULTS: In the paper, we present a Support Vector Machine based on Recursive Feature Elimination and Cross Validation (SVM-RFE-CV) algorithm for early detection of breast cancer in peripheral blood and show how to use SVM-RFE-CV to model the classification and prediction problem of early detection of breast cancer in peripheral blood.The training set which consists of 32 health and 33 cancer samples and the testing set consisting of 31 health and 34 cancer samples were randomly separated from a dataset of peripheral blood of breast cancer that is downloaded from Gene Express Omnibus. First, we identified the 42 differentially expressed biomarkers between "normal" and "cancer". Then, with the SVM-RFE-CV we extracted 15 biomarkers that yield zero cross validation score. Lastly, we compared the classification and prediction performance of SVM-RFE-CV with that of SVM and SVM Recursive Feature Elimination (SVM-RFE). CONCLUSIONS: We found that 1) the SVM-RFE-CV is suitable for analyzing noisy high-throughput microarray data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance (Area Under Curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the biomarkers are associated with Signaling, Hemostasis, Hormones, and Immune System, which are consistent with previous findings. Our prediction model can serve as a general model for biomarker discovery in early detection of other cancers. In the future, Polymerase Chain Reaction (PCR) is planned for validation of the ability of these potential biomarkers for early detection of breast cancer.
| S-EPMC3552693 | biostudies-literature
Project description:Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.
| S-EPMC7652278 | biostudies-literature