Article
Biochemistry & Molecular Biology
Sofia Tsouka, Mojgan Masoodi
Summary: Pathway analysis is commonly used for functional interpretation of metabolomics data, but lacks standardization and understanding of its impact on functional outcome. In this study, we investigated the consideration of non-human native enzymatic reactions and interconnectivity of pathways in pathway analysis. Exclusion of non-human reactions resulted in loss of information, while considering pathway connectivity provided better emphasis on certain metabolites but occasionally overemphasized central compounds. We proposed a penalization scheme to mitigate the effect of such compounds. We also performed over-representation analysis to compare and assess different methodologies. Our findings raise awareness on the capabilities and shortcomings of current pathway analysis practices in metabolomics.
Review
Biochemistry & Molecular Biology
Adam Amara, Clement Frainay, Fabien Jourdan, Thomas Naake, Steffen Neumann, Elva Maria Novoa-del-Toro, Reza M. Salek, Liesa Salzer, Sarah Scharfenberg, Michael Witting
Summary: Both targeted and untargeted mass spectrometry-based metabolomics approaches can be used to understand metabolic processes through network analysis. Network-based methods can provide insights into metabolism by connecting metabolites based on various relationships. These methods can address the challenges in metabolite identification and interpretation in untargeted metabolomics data analysis.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ying Shen, Huizhi Li, Dagang Li, Jingwei Zheng, Wenmin Wang
Summary: The study focuses on graph representation learning, proposing a new learning scheme (ANGraph) that better preserves the characteristics of graph structures and achieves significant performance improvement in node classification tasks.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Fabio Mercorio, Mario Mezzazanica, Vincenzo Moscato, Antonio Picariello, Giancarlo Sperli
Summary: The framework proposed in this article, named DICO, identifies overlapped communities of authors from Big Scholarly Data by modeling authors' interactions. DICO has three distinctive characteristics: a novel approach for building coauthorship network, a new community detection algorithm based on Node Location Analysis, and provided built-in queries.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Automation & Control Systems
Sandia Machado, Luisa Barreiros, Antonio R. Graca, Ricardo N. M. J. Pascoa, Marcela A. Segundo, Joao A. Lopes
Summary: In metabolomics, the complexity of data generated by untargeted approaches poses challenges in extracting meaningful information from raw data. Existing tools may overprocess the data, leading to the elimination of useful information. This research proposes a data mining tool for metabolomics data, specifically LC-MS, to enhance the extraction of meaningful chemical information. The algorithm performs well in identifying chemically relevant features and reduces the need for user-defined parameters when compared to existing software.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Review
Biochemical Research Methods
Yuguo Zha, Kang Ning
Summary: This article introduces the ontology-aware neural network (ONN) as a new framework for microbiome data mining, emphasizing its advantages in mining efficiency and accuracy, and highlighting its characteristic of knowledge discovery.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Penghui Xie, Guangyou Zhou, Jin Liu, Jimmy Xiangji Huang
Summary: FKGC aims to predict missing parts of a query triplet based on a small number of known samples. Existing approaches face challenges in effectively encoding remote neighbor information and modeling uncertainty of few-shot relations. To address these challenges, a global-local neighbor encoding module is proposed, along with an adaptive Gaussian mixture model for modeling few-shot relations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xinye Chen, Stefan Guttel
Summary: Symbolic representations are useful for dimension reduction of temporal data and enhance machine learning algorithms on time series. The adaptive Brownian bridge-based aggregation (ABBA) method accurately captures trends and shapes in time series but struggles with large datasets. We introduce fABBA, a new variant that reduces computational complexity and does not require the number of symbols to be specified in advance. Extensive tests show that fABBA outperforms ABBA in terms of runtime and reconstruction accuracy, and it can also compress other data types like images.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Information Systems
Xingyu Wu, Zhenchao Tao, Bingbing Jiang, Tianhao Wu, Xin Wang, Huanhuan Chen
Summary: Machine learning has been successful in analyzing biomedical data. However, the lack of samples in the biomedical field poses challenges for traditional variable selection algorithms. This paper proposes a method that utilizes domain knowledge to overcome this issue and demonstrates its effectiveness.
INFORMATION SCIENCES
(2022)
Article
Biochemical Research Methods
Jordi Rodeiro, Ester Vidana-Vila, Joan Navarro, Roger Mallol
Summary: This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly in MetaboAnalyst or Workflows4Metabolomics.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Yufei Liu, Guan Wang, Yuan Zhou, Yuhan Liu
Summary: This study proposes a novel framework for analyzing the evolutionary pathways of advanced technologies in the emerging field of nanogenerators. By calculating the similarity between clusters of different layers, the evolutionary pathways from grants to papers and then to patents are drawn, monitoring the development of established technologies and identifying emerging technologies under research.
Article
Chemistry, Multidisciplinary
Di Zhou, Wenjia Zhu, Tao Sun, Yang Wang, Yi Chi, Tianlu Chen, Jingchao Lin
Summary: Metabolomics data analysis heavily relies on bioinformatics tools, and the development of integrated platforms like IP4M and iMAP have met the evolving needs of researchers. iMAP offers extended functions, improved performances, and a new module with automatic pipeline and editable parameters for users, making it a valuable alternative tool for metabolomics data analysis.
FRONTIERS IN CHEMISTRY
(2021)
Article
Green & Sustainable Science & Technology
Viktor Sebestyen, Janos Abonyi
Summary: Since the declaration of the Sustainable Development Goals (SDGs) in 2015, countries have started developing national pathways for implementing the 2030 Agenda. Most countries have been successful in SDG12 and SDG10, but face challenges with SDG6, SDG2, and SDG1 on a global scale.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Maryam Maslek Elayam, Cyril Ray, Christophe Claramunt
Summary: This paper presents a hierarchical graph-based model for representing moving objects and trajectories, which is implemented and experimented with historical maritime data. Experimental analyses reveal knowledge patterns from the hierarchical graph database, and queries applied to an European maritime network derive mobility patterns and highlight network structures.
DATA & KNOWLEDGE ENGINEERING
(2022)
Article
Engineering, Environmental
Edoardo Ramalli, Timoteo Dinelli, Andrea Nobili, Alessandro Stagni, Barbara Pernici, Tiziano Faravelli
Summary: Validation and analysis of experiments and models are crucial in various engineering fields. This study proposes a systematic and automated methodology that utilizes the concept of a 'data ecosystem' to provide comprehensive insights about experiments and predictive models. The methodology focuses on data assessment, model performance measurement, and behavior insight extraction through data science techniques. It can be applied to different domains where predictive models are validated against big data in chemical engineering.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Biochemical Research Methods
Sergio Picart-Armada, Wesley K. Thompson, Alfonso Buil, Alexandre Perera-Lluna
Summary: This study analyzed the statistical properties and bias of diffusion scores, finding that diffusion scores starting from binary labels are affected by label codification and have problem-dependent topological bias that can be removed by statistical normalization. Parametric and non-parametric normalization methods address the bias sources of mean value and variance, improving performance when the sought positive labels are not aligned with the bias. The decision on bias removal should be data-driven based on quantitative analysis of the bias and its relation to positive entities.
Article
Biochemical Research Methods
Josep Marin-Llao, Sarah Mubeen, Alexandre Perera-Lluna, Martin Hofmann-Apitius, Sergio Picart-Armada, Daniel Domingo-Fernandez
Summary: High-throughput screening generates large amounts of biological data, which can be challenging to interpret. Knowledge-driven methods and computational approaches, such as diffusion algorithms, are essential for analyzing the large and complex biological networks. The MultiPaths framework, consisting of two independent Python packages, facilitates network analysis by providing command line interface, reproducible examples, and documentation.
Article
Biochemistry & Molecular Biology
Norma Dahdah, Alba Gonzalez-Franquesa, Sara Samino, Pau Gama-Perez, Laura Herrero, Jose Carlos Perales, Oscar Yanes, Maria Del Mar Malagon, Pablo Miguel Garcia-Roves
Summary: The study found that different tissues have specific lipid profiles regulated by caloric intake and dietary composition. Under different dietary and lifestyle conditions, the lipidome of different tissues showed distinct patterns of changes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Multidisciplinary Sciences
Kolja Becker, Holger Klein, Eric Simon, Coralie Viollet, Christian Haslinger, German Leparc, Christian Schultheis, Victor Chong, Markus H. Kuehn, Francesc Fernandez-Albert, Remko A. Bakker
Summary: Through RNA sequencing of post-mortem human retinal samples, this study uncovers molecular pathways and cell-specific changes involved in the development of diabetic retinopathy. The research reveals that the progression of DR is not only associated with vascular changes, but also with additional mechanisms and cell types.
SCIENTIFIC REPORTS
(2021)
Article
Chemistry, Analytical
Maria Barranco-Altirriba, Pol Sola-Santos, Sergio Picart-Armada, Samir Kanaan-Izquierdo, Jordi Fonollosa, Alexandre Perera-Lluna
Summary: mWISE is an R package for context-based annotation of LC-MS data, utilizing an algorithm with three main steps: matching mass-to-charge ratio values to the KEGG database, clustering and filtering KEGG candidates, and building a final prioritized list. mWISE outperforms other available annotation algorithms in terms of both performance and computation time, with the chemical structures proposed by mWISE being closer to the original compounds.
ANALYTICAL CHEMISTRY
(2021)
Article
Multidisciplinary Sciences
Lorena Pantano, George Agyapong, Yang Shen, Zhu Zhuo, Francesc Fernandez-Albert, Werner Rust, Dagmar Knebel, Jon Hill, Carine M. Boustany-Kari, Julia F. Doerner, Joerg F. Rippmann, Raymond T. Chung, Shannan J. Ho Sui, Eric Simon, Kathleen E. Corey
Summary: This study used total RNA-Seq to investigate the molecular mechanisms of NAFLD and fibrosis, identifying gene expression clusters strongly correlated with fibrosis stage, as well as the loss of hepatocytes and gain of other cell types with advancing fibrosis. The study also elucidated informative gene signatures for the disease.
SCIENTIFIC REPORTS
(2021)
Article
Cell Biology
Maria Vinaixa, Sandra Canelles, Africa Gonzalez-Murillo, Vitor Ferreira, Diana Grajales, Santiago Guerra-Cantera, Ana Campillo-Calatayud, Manuel Ramirez-Orellana, Oscar Yanes, Laura M. Frago, Angela M. Valverde, Vicente Barrios
Summary: The research found that non-diabetic IRS2(-/-) mice exhibited elevated anti-inflammatory cytokines in the hypothalamus, while diabetic IRS2(-/-) mice displayed a proinflammatory profile. Additionally, non-diabetic mice showed increased enzyme activity, enhanced lipid synthesis, and elevated levels of polyunsaturated fatty acids.
Article
Cell Biology
Jelena Weckerle, Sergio Picart-Armada, Stephan Klee, Tom Bretschneider, Andreas H. Luippold, Wolfgang Rist, Christian Haslinger, Holger Schlueter, Matthew J. Thomas, Bartlomiej Krawczyk, Francesc Fernandez-Albert, Marc Kastle, Daniel Veyel
Summary: Metabolic and lipid regulation in idiopathic pulmonary fibrosis (IPF) is limited. Analysis of the metabolome and lipidome in the bleomycin mouse model of IPF revealed increased tissue turnover, energy production, and inflammatory processes. Age had limited influence on metabolic and lipidomic changes in the model.
DISEASE MODELS & MECHANISMS
(2022)
Article
Biochemical Research Methods
Roger Gine, Jordi CapeHades, Josep M. Badia, Dennis Vughs, Michaela Schwaiger-Haber, Theodore Alexandrov, Maria Vinaixa, Andrea M. Brunner, Gary J. Patti, Oscar Yanes
Summary: HERMES is a molecular-formula-oriented and peak-detection-free method that uses LC/MS1 information to optimize MS2 acquisition, improving the identification rate and biological specificity of metabolites.
Article
Physiology
Laura Brugnara, Ana Isabel Garcia, Serafin Murillo, Josep Ribalta, Guerau Fernandez, Susanna Marquez, Miguel Angel Rodriguez, Maria Vinaixa, Nuria Amigo, Xavier Correig, Susana Kalko, Jaume Pomes, Anna Novials
Summary: The aim of this study was to investigate the relationship between muscle carnosine and type 1 diabetes, as well as how it is influenced by physical activity. The results showed that patients with type 1 diabetes had higher levels of muscle carnosine, which were influenced by physical activity, clinical characteristics, and lipoprotein subfractions.
EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY
(2022)
Article
Medicine, General & Internal
Marina Canyelles, Antonio Perez, Alexandra Junza, Inka Minambres, Oscar Yanes, Helena Sarda, Noemi Rotllan, Josep Julve, Jose Luis Sanchez-Quesada, Mireia Tondo, Joan Carles Escola-Gil, Francisco Blanco-Vaca
Summary: This study evaluated the effects of glycemic control and bariatric surgery on TMAO and γ BB in newly diagnosed T2D patients and morbidly obese subjects, showing that neither glycemic control nor bariatric surgery improved the circulating concentrations of TMAO in these populations.
Article
Biology
Alejandro Sola-Garcia, Maria Angeles Caliz-Molina, Isabel Espadas, Michael Petr, Concepcion Panadero-Moron, Daniel Gonzalez-Moran, Maria Eugenia Martin-Vazquez, Alvaro Jesus Narbona-Perez, Livia Lopez-Noriega, Guillermo Martinez-Corrales, Raul Lopez-Fernandez-Sobrino, Lina M. Carmona-Marin, Enrique Martinez-Force, Oscar Yanes, Maria Vinaixa, Daniel Lopez-Lopez, Jose Carlos Reyes, Joaquin Dopazo, Franz Martin, Benoit R. Gauthier, Morten Scheibye-Knudsen, Vivian Capilla-Gonzalez, Alejandro Martin-Montalvo
Summary: A multiomic approach reveals that long-term exposure to the Acly inhibitor SB-204990 can modulate molecular mechanisms associated with aging. ATP-citrate lyase has a crucial role in cellular metabolism, integrating the protein, carbohydrate, and lipid metabolism. In vivo studies using untargeted metabolomics, transcriptomics, and proteomics show that SB-204990 regulates energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, and can prevent the development of metabolic abnormalities associated with an unhealthy diet.
COMMUNICATIONS BIOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Laia Bertran, Jordi Capellades, Sonia Abello, Joan Duran-Bertran, Carmen Aguilar, Salome Martinez, Fatima Sabench, Xavier Correig, Oscar Yanes, Teresa Auguet, Cristobal Richart
Summary: This study explores the importance of metabolomic analysis in nonalcoholic steatohepatitis (NASH) associated with obesity. The analysis of blood metabolites in morbidly obese women showed significant differences in lipid metabolites and derivatives between NASH and normal liver patients. These findings may contribute to identifying the main metabolic pathways related to NASH and could potentially be used as biomarkers in the diagnosis and follow-up of the disease.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Teresa Auguet, Laia Bertran, Jordi Capellades, Sonia Abello, Carmen Aguilar, Fatima Sabench, Daniel del Castillo, Xavier Correig, Oscar Yanes, Cristobal Richart
Summary: This study aimed to identify differences between metabolically healthy obese individuals and those with metabolic disorders by conducting a metabolomics analysis in women. The findings showed that women with morbid obesity had higher levels of choline and acylglycerols, and lower levels of bile acids, steroids, ceramides, glycosphingolipids, lysophosphatidylcholines, and lysophosphatidylethanolamines compared to the normal weight group. In women with morbid obesity and type 2 diabetes mellitus, increased levels of glutamate, propionyl-carnitine, bile acids, ceramides, lysophosphatidylcholine 14:0, phosphatidylinositols, and phosphoethanolamines were found, along with lower levels of Phe-Ile/Leu. These metabolites may serve as new factors for further study on the pathogenesis of obesity and associated type 2 diabetes mellitus in women.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemical Research Methods
Yasin Kaymaz, Florian Ganglberger, Ming Tang, Christian Haslinger, Francesc Fernandez-Albert, Nathan Lawless, Timothy B. Sackton
Summary: The study introduces a new cell type projection tool, HieRFIT, based on hierarchical random forests to improve classification accuracy and reduce incorrect predictions. The ensemble approach combining multiple random forest models in a hierarchical decision tree structure demonstrates improved accuracy, especially for inter-dataset tasks in real-life applications.