Comparison of feature selection and classification for MALDI-MS data

Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a “global” understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.

Feature selection and analysis on correlated gas sensor data with recursive feature elimination

This study proposes a product form feature (PFF) selection method based on a numerical definition-based approach (NDA) and the consumers' image perceptions (CIPs). In the proposed approach, NDA is used to generate an explicit numerical definition of the product form design and the corresponding CIPs are determined by means of a semantic differential experiment. A CIP prediction model is constructed using support vector regression (SVR) techniques. Finally, the feature selection method, namely SVR with support vector machine recursive feature elimination (SVM-RFE), is used to identify the critical form features. The validity of the feature selection method is demonstrated using a knife design for illustration purposes. The result shows that the proposed method provides product designers with a powerful tool for systematically selecting the critical form features and evaluating their respective effects on the consumers' image perceptions.

In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the curse of dimensionality. To get good classification performance, it is necessary to preprocess the gene expression data. Support vector machine recursive feature elimination (SVM-RFE) is a classical method for gene selection. However, SVM-RFE suffers from high computational complexity. To remedy it, this paper enhances SVM-RFE for gene selection by incorporating feature clustering, called feature clustering SVM-RFE (FCSVM-RFE). The proposed method first performs gene selection roughly and then ranks the selected genes. First, a clustering algorithm is used to cluster genes into gene groups, in each which genes have similar expression profile. Then, a representative gene is found to represent a gene group. By doing so, we can obtain a representative gene set. Then, SVM-RFE is applied to rank these representative genes. FCSVM-RFE can reduce the computational complexity and the redundancy among genes. Experiments on seven public gene expression datasets show that FCSVM-RFE can achieve a better classification performance and lower computational complexity when compared with the state-the-art-of methods, such as SVM-RFE.

Skin cancer is on the rise. Hence the accurate detection of cancerous lesions is of paramount importance in the treatment of this health condition. In this study, we present a computer vision framework that studies 10 skin lesion features including newly introduced morphological and texture features along with established in the literature. All features are extracted automatically from 90 lesion images. A two-class classification problem was applied to determine the most significant features of disease using three features selection methods, Support Vector Machines Recursive Feature Elimination (SVMRFE), Information Gain, and Correlation-based Feature Subset Selection. We found that the five features selected by SVMRFE provides the highest accuracy readings of 100% model, 84% leave-one-out, and 89% 10-fold cross validation (10×CV) than the other two methods. Comparing the selected features with those used by the Total Dermoscopy Score, we report consistencies, disagreements, and the contributions of the present work.

Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data

Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease

Automated High-Throughput Matrix Assisted Laser Desorption Ionization Mass Spectrometry Methodology for Formulation Assessment of Polyethylene-Glycol-Conjugated Cytokine Proteins

With the massive expansion of information in social media, a high demand exists for information retrieval techniques. The recognition of the named entity (NE) such as person, location, organization, etc. has emerged as one of the main tasks in natural language processing. Often, utilizing the entire feature set may not only be time-consuming but may also have a negative effect on performance. Due to the high number of features, it is difficult to identify the subset of features relevant to a given task. In this paper, we apply feature selection methods based on the support vector machine recursive feature elimination (SVM-RFE) to find the optimized feature set. Afterward, an optimized feature set combination is used to identify and classify named entities (NEs) based on the Support Vector Machine (SVM). The proposed method is evaluated using Darwish’s dataset (a publicly available benchmark for Arabic NER for social media). Experimental results demonstrate the effectiveness of feature selection in enhancing performance and outperform the most advanced systems

Reaction of zinc dithiophosphate with dimethylsulfoxide was used to analyze zinc dialkyldithophosphates and diaryldithiophosphates lubricating oil additives by matrix assisted laser desorption ionization mass spectrometry (MALDIMS) analysis. The MALDI-MS analysis was carried out by mixing the [Zn(DTP)(DMSO)[subscript 2]][superscript +] with various type of matrix such as 2,5-dihydroxybenzoic acid, dithranol and alpha-cyano-4-hydroxycinnamic acid. The mixture of [Zn(DTP)(DMSO)2]+and matrix was applied to the multiprobe and bombarded with laser to genarate ionic analyte. The following MALDI-MS conditions were found to provided the good MALDI-MS spectrum of [Zn(DTP)(DMSO)[subscript 2]][superscript +] : dithranol as a matrix with tetrahydrofuran as solvent, the ratio of [Zn(DTP)(DMSO)[subscript 2]][superscript +] : matrix was 1 : 75 by volume and laser power was 187 [micro]J. The MALDI-MS spectrum of zinc dialkyldithiophosphates showed the peaks between m/z 517 and 615 and zinc diaryldithiophosphates showed the peaks between m/z 697 and 795. The MALDI-MS analysis of [Zn(DTP)(DMSO)2][superscript +] provided information on the carbon number of the two alkyl chains (R1+R2) which is an individual fingerprint of zinc dithiophosphates for any given commercial lubricating oils. Therefore, the developed MALDI-MS was used to analyze zinc dithiophosphates in commercial lubricating oils.

Cyanobacteria have developed acclimation strategies to adapt to harsh environments, making them a model organism. Understanding the molecular mechanisms of tolerance to abiotic stresses can help elucidate how cells change their gene expression patterns in response to stress. Recent advances in sequencing techniques and bioinformatics analysis methods have led to the discovery of many genes involved in stress response in organisms. The Synechocystis sp. PCC 6803 is a suitable microorganism for studying transcriptome response under environmental stress. Therefore, for the first time, we employed two effective feature selection techniques namely and support vector machine recursive feature elimination (SVM-RFE) and LASSO (Least Absolute Shrinkage Selector Operator) to pinpoint the crucial genes responsive to environmental stresses in Synechocystis sp. PCC 6803. We applied these algorithms of machine learning to analyze the transcriptomic data of Synechocystis sp. PCC 6803 under distinct conditions, encompassing light, salt and iron stress conditions. Seven candidate genes namely sll1862, slr0650, sll0760, slr0091, ssl3044, slr1285, and slr1687 were selected by both LASSO and SVM-RFE algorithms. RNA-seq analysis was performed to validate the efficiency of our feature selection approach in selecting the most important genes. The RNA-seq analysis revealed significantly high expression for five genes namely sll1862, slr1687, ssl3044, slr1285, and slr0650 under ion stress condition. Among these five genes, ssl3044 and slr0650 could be introduced as new potential candidate genes for further confirmatory genetic studies, to determine their roles in their response to abiotic stresses.

BackgroundEndometriosis (EM) is associated with immune dysregulation, while dysfunction of natural killer (NK) cells is regarded as a key mechanism underlying immune escape and the persistent growth of ectopic lesions.MethodThis study used single‐cell RNA sequencing (scRNA‐seq) on lesions from three patients with EM and on three normal endometrium samples and integrated these data with three bulk RNA‐seq datasets from GEO (GSE105765, GSE7305, and GSE6364). Seurat, Monocle, limma, least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM‐RFE) were used for cell clustering, trajectory inference, differential expression analysis, and feature selection. Immune‐cell composition and pathway activity were evaluated with CIBERSORT and GSVA. Gene expression was validated by qPCR, and cell migration and invasiveness were assessed using wound healing and Transwell assays.ResultscRNA‐seq resolved 11 clusters assigned to eight major cell types. By integrating pseudotime features with bulk data, 20 differentially expressed genes (DEGs) were prioritized, and machine‐learning analyses identified three key genes: granulysin (GNLY), perforin 1 (PRF1), and ENTPD1. The three‐gene model showed good discrimination in the training set and two external validation cohorts (AUCs 0.84, 0.67, and 0.77, respectively). GNLY and PRF1 were predominantly expressed in NK cells and CD8+ T cells and correlated with activation signatures, whereas ENTPD1 was highly expressed in endometrial stromal cells and enhanced their migratory and invasive capacities. ENTPD1 may contribute to disease via adenosine signaling–mediated modulation of NK–cell function. In silico analyses also nominated candidate agents targeting this pathway, including resveratrol, ibuprofen, and danazol.ConclusionThis study highlights the central role of NK–cell dysfunction in EM pathogenesis and proposes GNLY, PRF1, and ENTPD1 as potential molecular diagnostic biomarkers. Notably, ENTPD1 appears to have dual functions, including immunomodulation and promotion of stromal cell migration, which promotes lesion formation. These findings provide a mechanistic rationale and actionable targets for earlier screening and targeted therapy in EM.

BackgroundEndometriosis is recognized as an estrogen-dependent inflammation disorder, estimated to affect 8%-15% of women of childbearing age. Currently, the etiology and pathogenesis of endometriosis are not completely clear. Underlying mechanism for endometriosis is still under debate and needs further exploration. The involvement of transcription factors and immune mediations may be involved in the pathophysiological process of endometriosis, but the specific mechanism remains to be explored. This study aims to investigate the underlying molecular mechanisms in endometriosis.MethodsThe gene expression profile of endometriosis was obtained from the gene expression omnibus (GEO) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied to the endometriosis GSE7305 datasets. Cibersort and MCP-counter were used to explore the immune response gene sets, immune response pathway, and immune environment. Differentially expressed genes (DEGs) were identified and screened. Common biological pathways were being investigated using the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Transcription factors were from The Human Transcription Factors. The least absolute shrinkage and selection operator (Lasso) model identified four differential expressions of transcription factors (AEBP1, HOXB6, KLF2, and RORB). Their diagnostic value was calculated by receiver operating characteristic (ROC) curve analysis and validated in the validation cohort (GSE11691, GSE23339). By constructing the interaction network of crucial transcription factors, weighted gene coexpression network analysis (WGCNA) was used to search for key module genes. Metascape was used for enrichment analysis of essential module genes and obtained HOXB6, KLF2. The HOXB6 and KLF2 were further verified as the only two intersection genes according to Support Vector Machine Recursive Feature Elimination (SVM-RFE) and random forest models. We constructed ceRNA (lncRNA-miRNA-mRNA) networks with four potential transcription factors. Finally, we performed molecular docking for goserelin and dienogest with four transcription factors (AEBP1, HOXB6, KLF2, and RORB) to screen potential drug targets.ResultsImmune and metabolic pathways were enriched in GSVA and GSEA. In single sample gene set enrichment analysis (ssGSEA), most immune infiltrating cells, immune response gene sets, and immune response pathways are differentially expressed between endometriosis and non-endometriosis. Twenty-seven transcription factors were screened from differentially expressed genes. Most of the twenty-seven transcription factors were correlated with immune infiltrating cells, immune response gene sets and immune response pathways. Furthermore, Adipocyte enhancer binding protein 1 (AEBP1), Homeobox B6 (HOXB6), Kruppel Like Factor 2 (KLF2) and RAR Related Orphan Receptor B (RORB) were selected out from twenty-seven transcription factors. ROC analysis showed that the four genes had a high diagnostic value for endometriosis. In addition, KLF2 and HOXB6 were found to play particularly important roles in multiple modules (String, WGCNA, SVM-RFE, random forest) on the gene interaction network. Using the ceRNA network, we found that NEAT1 may regulate the expressions of AEBP1, HOXB6 and RORB, while X Inactive Specific Transcript (XIST) may control the expressions of HOXB6, RORB and KLF2. Finally, we found that goserelin and dienogest may be potential drugs to regulate AEBP1, HOXB6, KLF2 and RORB through molecular docking.ConclusionsAEBP1, HOXB6, KLF2, and RORB may be potential biomarkers for endometriosis. Two of them, KLF2 and HOXB6, are critical molecules in the gene interaction network of endometriosis. Discovered by molecular docking, AEBP1, HOXB6, KLF2, and RORB are targets for goserelin and dienogest.

Selecting distinguishable features from small sample data of rolling bearings has become an emerging and trending research topic in recent years. In this paper, a two-stage feature selection method based on Euclidean distance and multi-class support vector machine is proposed to remove irrelevant features and redundant features. First, the fault data are formed to a multidomain feature set. Second, the ReliefF algorithm based on Euclidean distance is used to remove irrelevant features. Third, the support vector machine recursive feature elimination (SVMRFE) is used to remove redundant features. Lastly, the KNN classifier is used to verify the effectiveness of this method. This paper uses the rolling bearing public datasets. After comparison, the results prove that the method proposed in the article is better.

Papers Presented at the 2005 Meeting of the Musculoskeletal Tumor Society