Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.

Computers in biology and medicine - 1 May 2024

Ameli Ali, Peña-Castillo Lourdes, Usefi Hamid

Abstract excerpt

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers
Share to Databases, Genetic
Share to Genetic Markers

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Parkinson Disease

Share to Parkinson's Disease Mechanisms and Treatments

Share to Polymorphism, Single Nucleotide

Share to Reproducibility of Results

Source

Identifiers

PMID: 38603902
DOI: 10.1016/j.compbiomed.2024.108407

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.PMID 38603902Read ↗

From GWAS to clinical utility in Parkinson's disease.Semantic proximity Read ↗

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning ApproachSemantic proximity Read ↗

Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s diseaseSemantic proximity Read ↗

The Quest for a Universal Parkinson’s Transcriptomics Signature is Derailed by Inherent Variability between PatientsSemantic proximity Read ↗

Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson’s Disease statusSemantic proximity Read ↗

Directional Gene-Level Concordance and Methodological Constraints in Blood Transcriptomic and DNA Methylation Studies of Parkinson’s DiseaseSemantic proximity Read ↗

An Integrative Network Approach for Longitudinal Stratification in Parkinson’s DiseaseSemantic proximity Read ↗

Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approachSemantic proximity Read ↗

Multi-Modality Machine Learning Predicting Parkinson’s DiseaseSemantic proximity Read ↗

Deep phenotyping for precision medicine in Parkinson's disease.Semantic proximity Read ↗

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's diseaseSemantic proximity Read ↗

Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s diseaseSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.