Hierarchical network analysis of co-occurring bioentities in literature.

Biomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles.

α-Glucosidic hydroquinone derivatives from Viburnum erosum.

Six undescribed compounds (1-6) were isolated from the leaves of Viburnum erosum along with four known compounds 7-10. The structures were determined by NMR and MS spectroscopic analyses, and their absolute configurations were established by chemical and spectroscopic methods. Compounds 1-6 were α-glucosidic hydroquinone derivatives with different linear monoterpenoid structures.

Targeted Isolation of Cytotoxic Sesquiterpene Lactones from by the NMR Annotation Tool, SMART 2.0.

Small Molecular Accurate Recognition Technology (SMART 2.0) has recently been introduced as a NMR-based machine learning tool for the discovery and characterization of natural products. We attempted targeted isolation of sesquiterpene lactones from with the aid of structural annotation by SMART 2.

The Rapid Discrimination and Quality Assessment of Three Species Using H NMR Spectrometry.

The husks and fruits of species (Rutaceae) are the popular pungent and spicy ingredients of foods and the traditional medicines in many countries. Three species, . , .

Exploring novel secondary metabolites from natural products using pre-processed mass spectral data.

Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources.

In silico annotation of discriminative markers of three Zanthoxylum species using molecular network derived annotation propagation.

In liquid chromatography-mass spectrometry (LC-MS) metabolomics, data matrices with up to thousands of variables for each ion peak are subjected to multivariate analysis (MVA) to assess the homogeneity between samples. The large dimensions of LC/MS datasets hinder the identification of the discriminant or the metabolic markers. In the present study, the molecular network (MN) approach and two in silico annotation tools, network annotation propagation (NAP) and the hierarchical chemical classification method, ClassyFire, were used to annotate the metabolites of three Zanthoxylum species, Z.