Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning.

Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected.

Artificial Intelligence-Based Medical Data Mining.

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

Biological and Genetic Characterization of Physostegia Chlorotic Mottle Virus in Europe Based on Host Range, Location, and Time.

Application of high throughput sequencing (HTS) technologies enabled the first identification of Physostegia chlorotic mottle virus (PhCMoV) in 2018 in Austria. Subsequently, PhCMoV was detected in Germany and Serbia on tomatoes showing severe fruit mottling and ripening anomalies. We report here how prepublication data-sharing resulted in an international collaboration across eight laboratories in five countries, enabling an in-depth characterization of PhCMoV. The independent studies converged toward its recent identification in eight additional European countries and confirmed its presence in samples collected 20 years ago (2002). The natural plant host range was expanded from two to nine species across seven families, and we confirmed the association of PhCMoV presence with severe fruit symptoms on economically important crops such as tomato, eggplant, and cucumber. Mechanical inoculations of selected isolates in the greenhouse established the causality of the symptoms on a new indexing host range. In addition, phylogenetic analysis showed a low genomic variation across the 29 near-complete genome sequences available. Furthermore, a strong selection pressure within a specific ecosystem was suggested by nearly identical sequences recovered from different host plants through time. Overall, this study describes the European distribution of PhCMoV on multiple plant hosts, including economically important crops on which the virus can cause severe fruit symptoms. This work demonstrates how to efficiently improve knowledge on an emergent pathogen by sharing HTS data and provides a solid knowledge foundation for further studies on plant rhabdoviruses.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Molecular characterisation of a new tenuivirus from Festuca sp.

A novel virus with a quadruple genome of negative-sense, single-stranded RNA was identified by high-throughput sequencing (HTS) in a grass sample from Saxony-Anhalt, Germany, and tentatively called Festuca stripe-associated virus (FSaV). The genome of FSaV consists of four segments and a total of 16,535 nucleotides (nt) which encode seven open reading frames (ORF). FSaV shares highest nt identity (between 72.84% to 80.74%) to Iranian wheat stripe virus (IWSV) and rice hoja blanca virus (RHBV). Additionally, pairwise comparisons between the amino acid sequences of the ORFs on the genome of FSaV and the corresponding ones on the genomes of the members of the Tenuvirus genus showed that FSaV shared 83.17% and 90.85% (amino acid) aa identity to IWSV. Moreover, the non-coding intergenic regions (ncIR) shared only between 49.5% to 60.87% nt identity to the corresponding regions on the IWSV genome. Based on the ICTV species demarcation, the results suggest that FSaV may represent a new species of the genus Tenuivirus. Plastid sequence analysis of the HTS data showed that the original host is a member of the genus Festuca most likely the species Festuca pratensis.