Refine
Document Type
- Article (2) (remove)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Random forest (2) (remove)
Institute
- Medizin (2)
Loss of neuronal stimulation enhances protein breakdown and reduces protein synthesis, causing rapid loss of muscle mass. To elucidate the pathophysiological adaptations that occur in atrophying muscles, we used stable isotope labelling and mass spectrometry to quantify protein expression changes accurately during denervation-induced atrophy after sciatic nerve section in the mouse gastrocnemius muscle. Additionally, mice were fed a stable isotope labelling of amino acids in cell culture (SILAC) diet containing 13C6-lysine for 4, 7 or 11 days to calculate relative levels of protein synthesis in denervated and control muscles. Ubiquitin remnant peptides (K-ε-GG) were profiled by immunoaffinity enrichment to identify potential substrates of the ubiquitin-proteasomal pathway. Of the 4279 skeletal muscle proteins quantified, 850 were differentially expressed significantly within 2 weeks after denervation compared with control muscles. Moreover, pulse labelling identified Lys6 incorporation in 4786 proteins, of which 43 had differential Lys6 incorporation between control and denervated muscle. Enrichment of diglycine remnants identified 2100 endogenous ubiquitination sites and revealed a metabolic and myofibrillar protein diglycine signature, including myosin heavy chains, myomesins and titin, during denervation. Comparative analysis of these proteomic data sets with known atrogenes using a random forest approach identified 92 proteins subject to atrogene-like regulation that have not previously been associated directly with denervation-induced atrophy. Comparison of protein synthesis and proteomic data indicated that upregulation of specific proteins in response to denervation is mainly achieved by protein stabilization. This study provides the first integrated analysis of protein expression, synthesis and ubiquitin signatures during muscular atrophy in a living animal.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.