Variant effect predictions capture some aspects of deep mutational scanning experiments

Background Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants – missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. Results On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. Conclusions DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization

Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s Disease and Essential Tremor

Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2+/-3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8+/-9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor.Peer ReviewedPostprint (published version