Large-scale prediction of adverse drug reactions-related proteins with network embedding.

MOTIVATION: Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations. However, the identification of ADR-related proteins on a large scale with high reliability remains an important challenge. RESULTS: In this article, we suggest a computational approach, Large-scale ADR-related Proteins Identification with Network Embedding (LAPINE). LAPINE combines a novel concept called single-target compound with a network embedding technique to enable large-scale prediction of ADR-related proteins for any proteins in the protein-protein interaction network. Analysis of benchmark datasets confirms the need to expand the scope of potential ADR-related proteins to be analyzed, as well as LAPINE's capability for high recovery of known ADR-related proteins. Moreover, LAPINE provides more reliable predictions for ADR-related proteins (Value-added positive predictive value = 0.12), compared to a previously proposed method (P < 0.001). Furthermore, two case studies show that most predictive proteins related to ADRs in LAPINE are supported by literature evidence. Overall, LAPINE can provide reliable insights into the relationship between ADRs and proteomes to understand the mechanism of ADRs leading to their prevention. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub (https://github.com/rupinas/LAPINE) and Figshare (https://figshare.com/articles/software/LAPINE/21750245) to facilitate its use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multidimensional Aspects of de qi Sensations in MASS and ASQ Assessment: A Pilot Study.

Background. De qi comprises varied senses depending on the individual. No single method can yet fully measure the multiple dimensions of de qi adequately. Objective. We examined the advantages of implementing multiple questionnaires for de qi measurement. Methods. Fourteen participants completed a preacupuncture questionnaire regarding their perception toward acupuncture treatment. After acupuncture stimulation at the HT7 point, de qi sensations were measured by MASS and ASQ. In groups with different levels of expectation, we compared the subtotal scores of each phase in the ASQ, as well as the VAS de qi intensity and MASS index using Kruskal-Wallis test. For the structural comparison of questionnaires, we first performed Spearman's rank correlation test between the scores of individual descriptors in MASS and ASQ. The subtotal scores of each phase in ASQ was compared with VAS de qi intensity and MASS index. Results. The subtotal score of the manipulation phase in ASQ strongly correlated with the VAS score of de qi intensity (Spearman's ρ = 0.654, p = 0.011) and MASS index (Spearman's ρ = 0.488, p = 0.076). MASS and ASQ showed strong correlations in certain analogous descriptors. Unpleasant perceptions toward acupuncture treatment did not significantly correlate with overall de qi intensity. Conclusions. De qi sensations in acupuncture treatment have multidimensional aspects. Intensity of stimulation, ASQ, and MASS index assess somewhat restricted aspects of de qi. Those questionnaires have exclusive differences of sets in spite of their strong intersections. Use of multiple questionnaires may enable a more comprehensive understanding of de qi properties and the elicitation of relevant construction in de qi features of acupuncture.