Biology subjects

Uppal, K.

Publications and source records attributed to Uppal, K..

4 recordsLinked to original sources

bioRxiv2018

Low-dose cadmium potentiates lung inflammatory response to 2009 pandemic H1N1 influenza virus in mice

BACKGROUNDCadmium (Cd) is a toxic, pro-inflammatory metal ubiquitous in the diet that accumulates in body organs due to inefficient elimination. Many individuals exposed to dietary Cd are also infected by seasonal influenza virus. The H1N1 strain causes mild to severe pneumonia which can be fatal.\n\nOBJECTIVESTo determine the influence of low-dose Cd exposure on inflammatory responses to H1N1 influenza A virus.\n\nMETHODSWe exposed mice to low-dose (1 mg CdCl2/l) Cd or vehicle (water) for 16 weeks prior to infection with a sub-lethal dose of H1N1. Lung inflammation was assessed by histopathology and flow cytometry. We used a combination of transcriptomics, metabolomics and bioinformatics to determine the molecular associations of inflammatory cells important in Cd-exacerbated responses.\n\nRESULTSCd-treated mice had increased lung tissue inflammatory cells, including neutrophils, monocytes, T lymphocytes and dendritic cells, following H1N1 infection. Lung genetic responses to infection (increasing TNF-a, interferon and complement, and decreasing myogenesis) were also exacerbated. Global correlations with immune cell counts, leading edge gene transcripts and metabolites revealed that Cd increased correlation of myeloid immune cells with pro-inflammatory genes, particularly interferon-{gamma}, and metabolites in amino acid, nucleobase, glycerophospholipid and vitamin B3 pathways.\n\nDISCUSSIONCd burden in mice increased inflammation in response to sub-lethal H1N1 challenge, which was coordinated by genetic and metabolic responses, and could provide new targets for intervention against lethal inflammatory pathology of clinical H1N1 infection.

pharmacology and toxicology↗

bioRxiv2017

optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment

MotivationRecent studies have shown that the ensemble feature selection approaches are essential for generating robust classifiers. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and do not account for classification accuracy while selecting the optimal set. Here we present a two-stage ensemble feature selection framework for finding the optimal set of features without compromising on classification accuracy.\n\nMethods and ResultsWe present herein optSelect, a multi agent-based stochastic optimization approach for nested ensemble feature selection. Stage one involves function perturbation, where ranked list of features are generated using different methods and stage two involves data perturbation, where feature selection is performed within randomly selected subsets of the training data and the optimal set of features is selected within each set using the optSelect. 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 the system performance using the random probe method and using five publicly available microarray datasets. The performance of optSelect is compared with single feature selection techniques and existing aggregation methods. The results show that the optSelect algorithm improves the classification accuracy compared to both individual and existing rank aggregation methods. The algorithm is incorporated into an R package, optSelect.\n\nContactkuppal2@emory.edu

bioinformatics↗

bioRxiv2017

SEACOIN2.0: an interactive mining and visualization tool for information retrieval, summarization, and knowledge discovery

MotivationThe 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 inferring topological and network relationships between biomedical terms. Very often too much is returned during summarization leading to information overload.\n\nResultsWe present herein SEACOIN 2.0, an interactive knowledge discovery and hypothesis generation tool for biomedical literature.SEACOIN generates k-ary relational networks of biomedical terms using a novel term ranking scheme to facilitate efficient information retrieval, summarization, and visual data exploration. Summarization is presented via multiple dynamic visualization panels. We evaluate the system performance in information retrieval and features extraction using the BioCreative 2013 Track 3 learning corpus. An average F-measure of 94% was achieved for document retrieval and an average precision of 88% was achieved for identification of top co-occurrence terms. The system allows interactive mining of complex implicit and explicit relationships among biomedical entities (genes, chemicals, diseases/disorders, mutations, etc.) and provides a framework for hypothesis generation. It also improves our understanding of various biological processes and disease mechanisms.\n\nContacteva.lee@gatech.edu

bioinformatics↗

bioRxiv2017

xMWAS: an R package for data-driven integration and differential network analysis

SummaryIntegrative 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. Further-more, 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, an R package for data integration, network visualization, clustering, differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.\n\nAvailabilityhttps://sourceforge.net/projects/xmwas/\n\nContactkuppal2@emory.edu

bioinformatics↗