scholarly journals SNPs rs11240569, rs708727, and rs823156 in SLC41A1 Do Not Discriminate Between Slovak Patients with Idiopathic Parkinson’s Disease and Healthy Controls: Statistics and Machine-Learning Evidence

2019 ◽  
Vol 20 (19) ◽  
pp. 4688
Author(s):  
Michal Cibulka ◽  
Maria Brodnanova ◽  
Marian Grendar ◽  
Milan Grofik ◽  
Egon Kurca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Susceptibility Locus ◽  
Healthy Controls ◽  
Diverse Populations ◽  
Slovak Population ◽  
Asian Populations ◽  
Clinical Diagnostic

Gene SLC41A1 (A1) is localized within Parkinson’s disease-(PD)-susceptibility locus PARK16 and encodes for the Na+/Mg2+-exchanger. The association of several A1 SNPs with PD has been studied. Two, rs11240569 and rs823156, have been associated with reduced PD-susceptibility primarily in Asian populations. Here, we examined the association of rs11240569, rs708727, and rs823156 with PD in the Slovak population and their power to discriminate between PD patients and healthy controls. The study included 150 PD patients and 120 controls. Genotyping was performed with the TaqMan® approach. Data were analyzed by conventional statistics and Random Forest machine-learning (ML) algorithm. Individually, none of the three SNPs is associated with an altered risk for PD-onset in Slovaks. However, a combination of genotypes of SNP-triplet GG(rs11240569)/AG(rs708727)/AA(rs823156) is significantly (p < 0.05) more frequent in the PD (13.3%) than in the control (5%) cohort. ML identified the power of the tested SNPs in isolation or of their singlets (joined), duplets and triplets to discriminate between PD-patients and healthy controls as zero. Our data further substantiate differences between diverse populations regarding the association of A1 polymorphisms with PD-susceptibility. Lack of power of the tested SNPs to discriminate between PD and healthy cases render their clinical/diagnostic relevance in the Slovak population negligible.

scholarly journals Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 242 ◽  
Author(s):  
Daniele Pietrucci ◽  
Adelaide Teofani ◽  
Valeria Unida ◽  
Rocco Cerroni ◽  
Silvia Biocca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Gut Microbiota ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Published Data ◽  
Promising Tool

The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

scholarly journals Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuya Uehara ◽  
Shin-Ichi Ueno ◽  
Haruka Amano-Takeshige ◽  
Shuji Suzuki ◽  
Yoko Imamichi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Alpha Synuclein ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Non Invasive

AbstractParkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.

Development of a Stacking-Based Ensemble Machine Learning for Detection of Depression in Parkinson’s Disease: Preliminary Research

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Haewon Byeon
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Depression Scale ◽  
Prognostic Index ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Outcome Variable

This preliminary study used the stacking ensemble to explore the major elements (factors) which could predict depression in patients with Parkinson’s disease and presented baseline data for developing a nomogram prognostic index for predicting high-risk groups for depression among patients with Parkinson’s disease in the future. Depression, an outcome variable, was divided into “with depression” and “without depression” using the Geriatric Depression Scale-30 (GDS-30). This study developed nine machine learning models (ANN, random forest, naive bayes, CART, ANN+LR, random forest+LR, naive bayes+LR, CART+LR, and random forest+naive bayes+CART+ANN+LR). The predictive performance (e.g., REMS, IA, Ev) of each machine learning model was validated through 10-fold cross-validation. The analysis results showed that the random forest+LR had the best predictive performance: RMSE = 0.16, IA = 0.73, and Ev = 0.48. This study analyzed the normalized importance of the random forest+LR model’s variables (the final model) and confirmed that K-MMSE, K-MoCA, Global CDR, sum of boxes in CDR, total score of UPDRS, motor score of UPDRS, K-IADL, H and Y staging, Schwab and England ADL, and REM and RBD were ten major variables with high weight among predictors of Parkinson’s disease with depression in South Korea. It is necessary as well to develop interpretable machine learning to build a model for predicting depression in patients with Parkinson’s disease that can be used in the medical field.

scholarly journals A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 421
Author(s):  
Satyabrata Aich ◽  
Jinyoung Youn ◽  
Sabyasachi Chakraborty ◽  
Pyari Mohan Pradhan ◽  
Jin-han Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Wearable Devices ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Healthcare Applications ◽  
Reported Data

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.

scholarly journals Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

2021 ◽  
Author(s):  
long qian ◽  
chaoyong xiao ◽  
Sidong Liu ◽  
zaixu cui ◽  
xiao hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Rating Scale ◽  
Mean Diffusivity ◽  
Learning Approach ◽  
Healthy Controls ◽  
White Matter Tracts ◽  
Machine Learning Approach

Abstract The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

scholarly journals Advanced brain ageing in Parkinson’s disease is related to disease duration and individual impairment

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Claudia R Eickhoff ◽  
Felix Hoffstaedter ◽  
Julian Caspers ◽  
Kathrin Reetz ◽  
Christian Mathys ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Duration ◽  
Reference Sample ◽  
Biological Age ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Brain Ageing ◽  
Brain Age

Abstract Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson’s disease. Here, we evaluate brain age in two cohorts of Parkinson’s patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson’s disease, newly diagnosed cases from the Parkinson’s Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson’s patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson’s disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

2021 ◽  
Author(s):  
Natalia Pelizari Novaes ◽  
Joana Bisol Balardin ◽  
Fabiana Campos Hirata ◽  
Luciano Melo ◽  
Edson Amaro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Healthy Controls ◽  
Global Efficiency ◽  
Motor Network
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