Treffer: Front Cover: MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization (Chem. Methods 12/2025).

Title:
Front Cover: MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization (Chem. Methods 12/2025).
Authors:
Mejri, Yassine, Cailloux, Olivier, Otogo N'Nang, Elvis, Séon‐Méniel, Blandine, Gallard, Jean‐François, Le Pogam, Pierre, Öztürk‐Escoffier, Meltem, Beniddir, Mehdi A.
Source:
Chemistry - Methods; Dec2025, Vol. 5 Issue 12, p1-1, 1p
Subject Terms:
NATURAL products, DECISION theory, DATA integration, TANDEM mass spectrometry, PYTHON programming language, MASS spectrometry
Database:
Complementary Index

Weitere Informationen

The article focuses on the development of MS2DECIDE, a Python library designed to enhance decision-making in the natural products discovery workflow driven by mass spectrometry. Created by a team of researchers, this library integrates outputs from three popular annotation tools—GNPS, SIRIUS, and ISDB-LOTUS—using decision theory and expert knowledge to prioritize natural products based on their potential novelty. The tool aims to streamline the process of identifying and evaluating natural products, providing valuable recommendations for researchers in the field. [Extracted from the article]

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