scholarly journals Predicting Parkinson’s Disease Related Genes Based on PyFeat and Gradient Boosted Decision Tree (GBDT)

2022 ◽  
Author(s):  
Marwa Helmy ◽  
Eman Eldaydamony ◽  
Nagham Mekky ◽  
Mohammed Elmogy ◽  
Hassan Soliman
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Performance Metrics ◽  
Gene Prediction ◽  
Critical Role ◽  
Area Under The Curve ◽  
Biomedical Analysis ◽  
Boosted Decision Tree ◽  
Disease Related Genes

Abstract Identifying genes related to Parkinson's disease (PD) is an active and effective research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. In recent years, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long non-coding (lncRNA) genes, which play an essential role in biological processes and the Transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the UCSC genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using five numerical mapping techniques with Fourier transform and PyFeat method with Adaboost technique as feature selection. Finally, the features were fed to the gradient boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics are used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy (ACC) equals 78.1%, the area under the curve (AUC) equals 84.9%, the area under precision-recall (AUPR) equals 85.0%, F1-score equals 78.2%, Matthews correlation coefficient (MCC) equals 0.564, Sensitivity (SEN) equals 79.1%, and specificity (SPC) equals 77.1%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.

scholarly journals N-semble-based method for identifying Parkinson’s disease genes

2021 ◽  
Author(s):  
Priya Arora ◽  
Ashutosh Mishra ◽  
Avleen Malhi
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gene Prediction ◽  
Gene Identification ◽  
Disease Genes ◽  
Multiple Model ◽  
Machine Learning Methods ◽  
Model Predictions ◽  
Disease Related Genes

AbstractParkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.

scholarly journals Parkin interacting substrate zinc finger protein 746 is a pathological mediator in Parkinson’s disease

Brain ◽  
2019 ◽  
Vol 142 (8) ◽  
pp. 2380-2401 ◽  
Author(s):  
Saurav Brahmachari ◽  
Saebom Lee ◽  
Sangjune Kim ◽  
Changqing Yuan ◽  
Senthilkumar S Karuppagounder ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Zinc Finger ◽  
Zinc Finger Protein ◽  
Critical Role ◽  
Trna Synthetase ◽  
Loss Of Function ◽  
Substrate Protein ◽  
Finger Protein ◽  
Sporadic Parkinson’S Disease

Abstract α-Synuclein misfolding and aggregation plays a major role in the pathogenesis of Parkinson’s disease. Although loss of function mutations in the ubiquitin ligase, parkin, cause autosomal recessive Parkinson’s disease, there is evidence that parkin is inactivated in sporadic Parkinson’s disease. Whether parkin inactivation is a driver of neurodegeneration in sporadic Parkinson’s disease or a mere spectator is unknown. Here we show that parkin in inactivated through c-Abelson kinase phosphorylation of parkin in three α-synuclein-induced models of neurodegeneration. This results in the accumulation of parkin interacting substrate protein (zinc finger protein 746) and aminoacyl tRNA synthetase complex interacting multifunctional protein 2 with increased parkin interacting substrate protein levels playing a critical role in α-synuclein-induced neurodegeneration, since knockout of parkin interacting substrate protein attenuates the degenerative process. Thus, accumulation of parkin interacting substrate protein links parkin inactivation and α-synuclein in a common pathogenic neurodegenerative pathway relevant to both sporadic and familial forms Parkinson’s disease. Thus, suppression of parkin interacting substrate protein could be a potential therapeutic strategy to halt the progression of Parkinson’s disease and related α-synucleinopathies.

scholarly journals Stability of MDS-UPDRS Motor Subtypes Over Three Years in Early Parkinson's Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Abhijeet K. Kohat ◽  
Samuel Y. E. Ng ◽  
Aidan S. Y. Wong ◽  
Nicole S. Y. Chia ◽  
Xinyi Choi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Baseline Factors ◽  
Prospective Cohorts ◽  
The Mean ◽  
The Stability ◽  
Early Parkinson’S Disease ◽  
Levodopa Equivalent Daily Dose

Background: Various classifications have been proposed to subtype Parkinson's disease (PD) based on their motor phenotypes. However, the stability of these subtypes has not been properly evaluated.Objective: The goal of this study was to understand the distribution of PD motor subtypes, their stability over time, and baseline factors that predicted subtype stability.Methods: Participants (n = 170) from two prospective cohorts were included: the Early PD Longitudinal Singapore (PALS) study and the National Neuroscience Institute Movement Disorders Database. Early PD patients were classified into tremor-dominant (TD), postural instability and gait difficulty (PIGD), and indeterminate subtypes according to the Movement Disorder Society's Unified PD Rating Scale (MDS-UPDRS) criteria and clinically evaluated for three consecutive years.Results: At baseline, 60.6% patients were TD, 12.4% patients were indeterminate, and 27.1% patients were PIGD subtypes (p < 0.05). After 3 years, only 62% of patients in TD and 50% of patients in PIGD subtypes remained stable. The mean levodopa equivalent daily dose (LEDD) was higher in the PIGD subtype (276.92 ± 232.91 mg; p = 0.01). Lower LEDD [p < 0.05, odds ratio (OR) 0.99, 95% confidence interval (CI): 0.98–0.99] and higher TD/PIGD ratios (p < 0.05, OR 1.77, 95% CI: 1.29–2.43) were independent predictors of stability of TD subtype with an area under the curve (AUC) of 0.787 (95%CI: 0.669–0.876), sensitivity = 57.8%, and specificity = 89.7%.Conclusion: Only 50–62% of PD motor subtypes as defined by MDS-UPDRS remained stable over 3 years. TD/PIGD ratio and baseline LEDD were independent predictors for TD subtype stability over 3 years.

scholarly journals Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson’s disease, and matched controls

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Vrutangkumar V. Shah ◽  
James McNames ◽  
Martina Mancini ◽  
Patricia Carlson-Kuhta ◽  
Rebecca I. Spain ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurological Disease ◽  
Inertial Sensors ◽  
Daily Life ◽  
Area Under The Curve ◽  
Gait Characteristics ◽  
Discrete Moment ◽  
Healthy Control

Abstract Background and purpose  Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson’s Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. Methods  We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann–Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. Results  Participants wore sensors for 60–68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63–0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69–1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59–0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70–0.98]). AUCs were larger in daily life compared to the laboratory. Conclusions Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.

scholarly journals The Role of α-Synuclein Oligomers in Parkinson’s Disease

2020 ◽  
Vol 21 (22) ◽  
pp. 8645
Author(s):  
Xiao-yu Du ◽  
Xi-xiu Xie ◽  
Rui-tian Liu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Theoretical Basis ◽  
Critical Role ◽  
Treatment Regimens ◽  
Propagation Effects ◽  
Different Types ◽  
Oligomeric Forms ◽  
Neurodegeneration Disease

α-synuclein (α-syn) is a protein associated with the pathogenesis of Parkinson’s disease (PD), the second most common neurodegeneration disease with no effective treatment. However, how α-syn drives the pathology of PD remains elusive. Recent studies suggest that α-syn oligomers are the primary cause of neurotoxicity and play a critical role in PD. In this review, we discuss the process of α-syn oligomers formation and the current understanding of the structures of oligomers. We also describe seed and propagation effects of oligomeric forms of α-syn. Then, we summarize the mechanism by which α-syn oligomers exert neurotoxicity and promote neurodegeneration, including mitochondrial dysfunction, endoplasmic reticulum stress, proteostasis dysregulation, synaptic impairment, cell apoptosis and neuroinflammation. Finally, we investigate treatment regimens targeting α-syn oligomers at present. Further research is needed to understand the structure and toxicity mechanism of different types of oligomers, so as to provide theoretical basis for the treatment of PD.

Recent advances in the biosensing of neurotransmitters: material and method overviews towards the biomedical analysis of psychiatric disorders

2020 ◽  
Vol 12 (4) ◽  
pp. 557-575 ◽  
Author(s):  
Ahmad Mobed ◽  
Mohammad Hasanzadeh ◽  
Ali Ahmadalipour ◽  
Ali Fakhari
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Nervous System ◽  
Psychiatric Disorders ◽  
Cognitive Disorders ◽  
Biomedical Analysis ◽  
Recent Advances

Neurotransmitters are the most important messengers of the nervous system, and any changes in their balances and activities can cause serious neurological, psychiatric and cognitive disorders such as schizophrenia, Alzheimer's disease and Parkinson's disease.

scholarly journals Diet in Parkinson's Disease: Critical Role for the Microbiome

2019 ◽  
Vol 10 ◽  
Author(s):  
Aeja Jackson ◽  
Christopher B. Forsyth ◽  
Maliha Shaikh ◽  
Robin M. Voigt ◽  
Phillip A. Engen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Critical Role

scholarly journals Alpha-Synuclein Protofibrils in Cerebrospinal Fluid: A Potential Biomarker for Parkinson's Disease

2020 ◽  
Vol 10 (4) ◽  
pp. 1429-1442
Author(s):  
Marianne von Euler Chelpin ◽  
Linda Söderberg ◽  
Johanna Fälting ◽  
Christer Möller ◽  
Marco Giorgetti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Single Molecule ◽  
Area Under The Curve ◽  
Corticobasal Degeneration ◽  
Alpha Synuclein ◽  
Cross Reactivity ◽  
Potential Biomarker ◽  
Csf Levels

Background: Currently, there is no established biomarker for Parkinson's disease (PD) and easily accessible biomarkers are crucial for developing disease-modifying treatments. Objective: To develop a novel method to quantify cerebrospinal fluid (CSF) levels of α-synuclein protofibrils (α-syn PF) and apply it to clinical cohorts of patients with PD and atypical parkinsonian disorders. Methods: A cohort composed of 49 patients with PD, 12 with corticobasal degeneration (CBD), 22 with progressive supranuclear palsy, and 33 controls, that visited the memory clinic but had no biomarker signs of Alzheimer’s disease (AD, tau<350 pg/mL, amyloid-beta 42 (Aβ42)>530 pg/mL, and phosphorylated tau (p-tau)<60 pg/mL) was used in this study. The CSF samples were analyzed with the Single molecule array (Simoa) technology. Total α-synuclein (α-syn) levels were analyzed with a commercial ELISA-kit. Results: The assay is specific to α-syn PF, with no cross-reactivity to monomeric α-syn, or the β- and γ-synuclein variants. CSF α-syn PF levels were increased in PD compared with controls (62.1 and 40.4 pg/mL, respectively, p = 0.03), and CBD (62.1 and 34.2 pg/mL, respectively, p = 0.02). The accuracy of predicting PD using α-syn PF is significantly different from controls (area under the curve 0.68, p = 0.0097) with a sensitivity of 62.8% and specificity of 67.7%. Levels of total α-syn were significantly different between the PD and CBD groups (p = 0.04). Conclusion: The developed method specifically quantifies α-syn PF in human CSF with increased concentrations in PD, but with an overlap with asymptomatic elderly controls.

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