HNOXPred: a web tool for the prediction of gas-sensing H-NOX proteins from amino acid sequence.

SUMMARY: HNOXPred is a webserver for the prediction of gas-sensing heme-nitric oxide/oxygen (H-NOX) proteins from amino acid sequence. H-NOX proteins are gas-sensing hemoproteins found in diverse organisms ranging from bacteria to eukaryotes. Recently, gas-sensing complex multi-functional proteins containing only the conserved amino acids at the heme centers of H-NOX proteins, have been identified through a motif-based approach. Based on experimental data and H-NOX candidates reported in the literature, HNOXPred is created to automate and facilitate the identification of similar H-NOX centers across systems. The server features HNOXSCORES scaled from 0 to 1 that consider in its calculation, the physicochemical properties of amino acids constituting the heme center in H-NOX in addition to the conserved amino acids within the center. From user input amino acid sequence, the server returns positive hits and their calculated HNOXSCORES ordered from high to low confidence which are accompanied by interpretation guides and recommendations. The utility of this server is demonstrated using the human proteome as an example. AVAILABILITY AND IMPLEMENTATION: The HNOXPred server is available at https://www.hnoxpred.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce.

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.

ProbResist: a database for drug-resistant probiotic bacteria.

Drug resistance remains a global threat, and the rising trend of consuming probiotic-containing foods, many of which harbor antibiotic resistant determinants, has raised serious health concerns. Currently, the lack of accessibility to location-, drug- and species-specific information of drug-resistant probiotics has hampered efforts to combat the global spread of drug resistance. Here, we describe the development of ProbResist, which is a manually curated online database that catalogs reports of probiotic bacteria that have been experimentally proven to be resistant to antibiotics. ProbResist allows users to search for information of drug resistance in probiotics by querying with the names of the bacteria, antibiotic or location. Retrieved results are presented in a downloadable table format containing the names of the antibiotic, probiotic species, resistant determinants, region where the study was conducted and digital article identifiers (PubMed Identifier and Digital Object Identifier) hyperlinked to the original sources. The webserver also presents a simple analysis of information stored in the database. Given the increasing reports of drug-resistant probiotics, an exclusive database is necessary to catalog them in one platform. It will enable medical practitioners and experts involved in policy making to access this information quickly and conveniently, thus contributing toward the broader goal of combating drug resistance. DATABASE URL: https://probresist.com.