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Item type:Publication, Graph embedding on mass spectrometry- and sequencing-based biomedical data(BioMed Central Ltd, 2024-12-01)Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings(Nature Research, 2024-12-01)Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based approach tends to overfilter raw data, which may result in the removal of relevant data and lead to the identification of fewer metabolomic changes. This limitation of the traditional approach underscores the need for a new method. In this work, we present a novel deep learning approach using node embeddings (powered by GNNs), edge embeddings, and anomaly detection algorithm to analyze the data generated by mass spectrometry (MS)-based metabolomics called GEMNA (Graph Embedding-based Metabolomics Network Analysis), for example for an untargeted volatile study on Mentos candy, the data clusters produced by GEMNA were better than the ones used traditional tools, i.e., GEMNA has, vs. the traditional approach has. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Unlocking the Secrets of Insects: The Role of Mass Spectrometry to Understand the Life of Insects(John Wiley and Sons Inc, 2025-07-01)Chemical signaling is crucial during the insect lifespan, significantly affecting their survival, reproduction, and ecological interactions. Unfortunately, most chemical signals insects use are impossible for humans to perceive directly. Hence, mass spectrometry has become a vital tool by offering vital insight into the underlying chemical and biochemical processes in various variety of insect activities, such as communication, mate recognition, mating behavior, and adaptation (defense/attack mechanisms), among others. Here, we review different mass spectrometry-based strategies used to gain a deeper understanding of the chemicals involved in shaping the complex behaviors among insects and mass spectrometry-based research in insects that have direct impact in global economic activities.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comprehensive review of the development of green extraction methods and encapsulation of theobromine from cocoa bean shells for nutraceutical applications(Springer, 2025)Cocoa bean shells (CBS) represent up to 20% of the waste from roasted beans in emerging countries, such as Peru, one of the leading producers of fine-aroma cocoa (Theobroma cacao L.) in the world. Due to the high phenolic and theobromine concentrations in agricultural residues such as cocoa bean shells (CBS), multidisciplinary research is focused on optimizing the extraction, characterization, and evaluation of phenolic compounds present in CBS. To provide a complete guide for the extraction of theobromine from CBS, we present here the main methods of extraction and stabilization (encapsulation) of theobromine present in CBS, moving from conventional techniques to others considered “green,” such as ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), supercritical fluid extraction (SFE), pressurized liquid extraction (PLE), even deep eutectic solvent extraction (DES), hydrodynamic cavitation reactors (HCR), pulsed electric field (PEF), and high-voltage electric discharge extraction (HVED), pressurized hot water extraction (PHWE) and subcritical water extraction (SCE), among others. Here, the significant increase in theobromine concentration of the extracts is highlighted, as well as the importance of microencapsulation and nanoencapsulation in protecting their bioactivity. The UAE and MAE methods are more effective for theobromine extraction, respectively. On the other hand, encapsulations have been evaluated primarily with maltodextrin mixed with gum Arabic, chitosan, and whey protein by spray drying or freeze-drying. It is concluded that obtaining a nutraceutical product from CBS in a sustainable circular agricultural economy requires optimizing scalable green extraction processes, such as US, and exploring new encapsulated materials and their mixtures to stabilize bioactive compounds, taking advantage of synergistic protection effects.13