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AI and data science using gradient boosting machine (GBM) learning and analytics is at the forefront of applied machine learning in life sciences and medicine, as part of the field of artificial intelligence (AI). Gradient boosting includes a toolkit of learning techniques that aim to build predictive models by combining the outputs of multiple less robust models using sequential decision trees. During a presentation at Analytica in Munich, Germany, experts discussed his use of GBM in life sciences and medicine.
The first talk of this session was given by Bing Zhang from Baylor College of Medicine in Houston, Texas, and was titled “Leveraging Artificial Intelligence to Unravel the Dark Phosphoproteome.” This talk addressed the challenges of effectively analyzing and interpreting mass spectrometry-based phosphoproteomics data. Zhang’s team used machine learning (ML) and deep learning (DL) techniques to enhance his data analysis of the phosphoproteome, aiming to understand what they called the “dark phosphoproteome.” Specifically, he developed his DeepRescore2 software, which utilizes deep learning-based retention time and fragment ion intensity prediction to improve phosphopeptide identification and phosphorylation site localization. In addition, Zhang will discuss his IDPpub computational pipeline that leverages BioBERT software to extract phosphorylation sites from biomedical abstracts and facilitates the identification of regulatory enzymes and biological functions of phosphorylation sites. did.
The second talk of this session was presented by Lennart Martens from VIB Institute of Life Sciences and Ghent University in Belgium. The presentation was titled “Machine Learning-Powered Floodlight Illuminating Precision Medicine” and focused on the integration of machine learning models into mass spectrometry-based proteomics. Martens highlighted that identification performance was significantly improved by combining machine learning models such as MS2PIP and his DeepLC software with his MS2Rescore variant of the Percolator scoring engine. These machine learning models enhance information recovery from proteomics data and provide new insights into the fundamental biology and pathology encoded in existing datasets. Additionally, Martens highlighted the potential of machine learning models in uncovering detailed insights into molecular pathology and mapping protein activity on a proteome-wide scale, which could impact precision medicine. .
The third presentation by Fan Liu from the Leibniz Institute for Molecular Pharmacology (FMP) in Berlin, Germany, will discuss “Development of structure interaction actomics and applications in cell biology,” and explore proteome-wide approaches to capture protein interactions. focused on cross-linking mass spectrometry. Spatial arrangement of molecules. Liu highlighted that advances in experimental methods and software in his tools have generated extensive protein-protein interaction (PPI) data across several biological systems. These data provide insight into the subcellular localization, interactions, and structure of proteins and identify specific amino acid sequences and structural features within proteins that play important roles in protein-protein interactions (PPIs). serves as valuable training data for AI-based methods. Mediates the bond between proteins,
The final talk of the session was given by AP Gamiz-Hernandez from Stockholm University, Sweden, who talked about the challenges in understanding cellular energy metabolism and OXPHOS (oxidative phosphorylation), “Protein Function and Disease Molecules.” “Insights into the Principle” was published. ) The energy conversion machinery of the complex is located in the inner mitochondrial membrane and is involved in the production of ATP (adenosine triphosphate) for cellular energy. Using a combination of molecular dynamics simulations and machine learning models, Gamiz-Hernandez found that the pKa We discussed predicting structure-based chemical reactivity such as redox and redox reactions. Possibility of protein. This approach aimed to identify key residues involved in protein function and disease-associated mutations and provide insight into the molecular principles underlying protein function and disease mechanisms.
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