The autoxidation of triglyceride (or triacylglycerol, TAG) is a poorly understood complex system. It is known from mass spectrometry measurements that, although initiated by a single molecule, this system involves an abundance of intermediate species and a complex network of reactions. For this reason, the attribution of the mass peaks to exact molecular structures is difficult without additional information about the system. We provide such information using a graph theory-based algorithm. Our algorithm performs an automatic discovery of the chemical reaction network that is responsible for the complexity of the mass spectra in drying oils. This knowledge is then applied to match experimentally measured mass spectra with computationally predicted molecular graphs. We demonstrate this methodology on the autoxidation of triolein as measured by electrospray ionization-mass spectrometry (ESI-MS). Our protocol can be readily applied to investigate other oils and their mixtures.
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http://dx.doi.org/10.1021/acs.jcim.0c01163 | DOI Listing |
Bioinformatics
January 2025
Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
Motivation: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
View Article and Find Full Text PDFIET Syst Biol
January 2025
School of Computer, University of South China, Hengyang, Hunan, China.
Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell-cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space.
View Article and Find Full Text PDFGlob Epidemiol
June 2025
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Introduction: Opium and cigarette smoking have been identified as significant cancer risk factors. Recently, the International Agency for Research on Cancer (IARC) classified opium as a Group 1 carcinogen in 2020.
Method: Using data from a multicenter case-control study in Iran called IROPICAN, involving 717 cases of bladder cancer and 3477 controls, we assessed the interactions on the causal additive scale between opium use and cigarette smoking and their attributing effects to evaluate public health relevance and test for different mechanistic interaction forms to provide new insights for developing of bladder cancer.
J Cheminform
January 2025
Department of Mathematics, University of Tennessee, Knoxville, TN, 37996, USA.
Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)-based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory.
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