Recent studies have shown that ensemble feature selection approaches can improve the robustness and stability of final classification models. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and are often based on metrics independent of the classification accuracy while selecting the optimal set. In this paper, we develop the OptSelect tool for ensemble feature selection and stability assessment of individual features for improved biomarker discovery. The software tool is packaged in R for broad dis-semination. OptSelect is a multi agent-based stochastic optimization tool designed for ensemble feature selection. Stage one involves function perturbation, where ranked list of features is generated using multiple feature selection methods. Stage two in-volves data perturbation, where feature selection is performed within randomly selected learning sets of the training da-ta. The agents are assigned to different behavior states and move according to a binary PSO algorithm. A multi-objective fitness function is used to evaluate the classification accuracy of the agents. We evaluate OptSelect system performance using the random probe method testing on five publicly available microarray datasets. The performance is compared with single feature selection techniques and existing aggregation methods. The results show that OptSelect improves classification accuracy when compared to both individual and existing rank aggregation methods. The PSO algorithm is able to uncover important discriminatory features for predicting COVID-19 disease severity, demonstrating its important role within the optSelect tool. The algorithm is incorporated into an R package and disseminated via GitHub: https://github.com/kuppa12/optSe1ect.

The rapidly increasing size of biomedical databases such as Medline requires the use of intelligent data mining methods for information extraction and summarization. Existing biomedical text-mining tools have limited capabilities for incorporating citation information during document ranking and for inferring topological and network relationships between biomedical terms. Often too much is returned during summarization leading to information overload. Furthermore, literature-based discoveries could be hard to interpret if the network is too complex. SEACOIN2.0 can incorporate citation information during document ranking and uses a unique association rule mining algorithm to generate multi-level k-ary trees. The multi-level trees facilitate efficient information retrieval, visual data exploration, summarization, and hypothesis generation. The system presents graphical summarization via multiple dynamic visualization panels and an interactive word cloud. LexRank algorithm is used to identify salient sentences in top abstracts related to the query. An average F-measure of 94% was achieved for document retrieval, and an average precision of 88% was obtained for identification of top co-occurrence terms. SEACOIN2.0 was also used to replicate previously published findings using the literature-based discovery and EMR-based PheWAS approaches. We present herein SEACOIN2.0 (https://newton.isye.gatech.edu/SEACOIN2/), an interactive visual mining tool for improved information retrieval, automated multi-level summarization of Medline abstracts, and literature-based discovery. SEACOIN2.0 addresses the problem of “information overload” and allows clinicians and biomedical researchers to meet their information needs.

Summary: Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms. Availability and implementation: https://kuppal.shinyapps.io/xmwas (Online) and https://github.com/kuppal2/xMWAS/ (R). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. RESULTS: xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. CONCLUSIONS: xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.