AIMC Topic: Succinate Dehydrogenase

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Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

The Journal of pathology Jul 1, 2020
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutation...
Humans Machine Learning Cohort Studies Head and Neck Neoplasms Metabolomics Adrenal Gland Neoplasms Mutation Immunohistochemistry Succinate Dehydrogenase Paraganglioma Pheochromocytoma
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Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.

Journal of chemical information and modeling Oct 20, 2015
The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method ca...
Humans Machine Learning Animals Membrane Proteins Databases, Protein Models, Chemical Bacterial Proteins Amino Acid Sequence Solubility Solvents Protein Structure, Secondary Molecular Sequence Data Sequence Alignment Protein Structure, Quaternary Succinate Dehydrogenase Wolinella
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