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.

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.

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.

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.