Cargando…

Machine learning analysis predicts a person’s sex based on mechanical but not thermal pain thresholds

Sex differences in pain perception have been extensively studied, but precision medicine applications such as sex-specific pain pharmacology have barely progressed beyond proof-of-concept. A data set of pain thresholds to mechanical (blunt and punctate pressure) and thermal (heat and cold) stimuli a...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lötsch, Jörn, Mayer, Benjamin, Kringel, Dario
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163041/ https://www.ncbi.nlm.nih.gov/pubmed/37147321 http://dx.doi.org/10.1038/s41598-023-33337-2

Ejemplares similares

Artificial intelligence and machine learning in pain research: a data scientometric analysis
por: Lötsch, Jörn, et al.
Publicado: (2022)

Machine Learning in Human Olfactory Research
por: Lötsch, Jörn, et al.
Publicado: (2019)

Next-generation sequencing of human opioid receptor genes based on a custom AmpliSeq™ library and ion torrent personal genome machine
por: Kringel, Dario, et al.
Publicado: (2016)

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes
por: Lötsch, Jörn, et al.
Publicado: (2020)

Machine-learned analysis of global and glial/opioid intersection–related DNA methylation in patients with persistent pain after breast cancer surgery
por: Kringel, Dario, et al.
Publicado: (2019)

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
por: Lötsch, Jörn, et al.
Publicado: (2018)

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain
por: Kringel, Dario, et al.
Publicado: (2021)

Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications
por: Lötsch, Jörn, et al.
Publicado: (2018)

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective
por: Lötsch, Jörn, et al.
Publicado: (2017)

Machine learning in pain research
por: Lötsch, Jörn, et al.
Publicado: (2018)

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis
por: Kringel, Dario, et al.
Publicado: (2021)

Machine-learned analysis of the association of next-generation sequencing–based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin
por: Kringel, Dario, et al.
Publicado: (2018)

Development of an AmpliSeq(TM) Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain
por: Kringel, Dario, et al.
Publicado: (2018)

Next-generation sequencing of the human TRPV1 gene and the regulating co-players LTB4R and LTB4R2 based on a custom AmpliSeq(™) panel
por: Kringel, Dario, et al.
Publicado: (2017)

A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes
por: Kringel, D., et al.
Publicado: (2018)

Multimodal Distribution of Human Cold Pain Thresholds
por: Lötsch, Jörn, et al.
Publicado: (2015)

Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments
por: Sipilä, Reetta, et al.
Publicado: (2020)

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
por: Miettinen, Teemu, et al.
Publicado: (2022)

Agreeable Smellers and Sensitive Neurotics – Correlations among Personality Traits and Sensory Thresholds
por: Croy, Ilona, et al.
Publicado: (2011)

Machine-learned cluster identification in high-dimensional data
por: Ultsch, Alfred, et al.
Publicado: (2017)

Machine-learned pattern identification in olfactory subtest results
por: Lötsch, Jörn, et al.
Publicado: (2016)

Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data
por: Lötsch, Jörn, et al.
Publicado: (2019)

A machine-learned analysis suggests non-redundant diagnostic information in olfactory subtests
por: Lötsch, Jörn, et al.
Publicado: (2019)

Sex differences in thermal detection and thermal pain threshold and the thermal grill illusion: a psychophysical study in young volunteers
por: Averbeck, Beate, et al.
Publicado: (2017)

Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)
por: Ultsch, Alfred, et al.
Publicado: (2015)

Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation
por: Lötsch, Jörn, et al.
Publicado: (2022)

Sorting of Odor Dilutions Is a Meaningful Addition to Assessments of Olfactory Function as Suggested by Machine-Learning-Based Analyses
por: Lötsch, Jörn, et al.
Publicado: (2022)

Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size
por: Lötsch, Jörn, et al.
Publicado: (2023)

Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data
por: Ultsch, Alfred, et al.
Publicado: (2022)

Heat Flux Sensing for Machine-Learning-Based Personal Thermal Comfort Modeling
por: Jung, Wooyoung, et al.
Publicado: (2019)

Machine Learning Refutes Loss of Smell as a Risk Indicator of Diabetes Mellitus
por: Lötsch, Jörn, et al.
Publicado: (2021)

Pain in the network of genetic and epigenetic control
por: Lötsch, Jörn, et al.
Publicado: (2014)

Effect of buspirone on thermal sensory and pain thresholds in human volunteers
por: Pavlaković, Goran, et al.
Publicado: (2009)

Prediction of thermal boundary resistance by the machine learning method
por: Zhan, Tianzhuo, et al.
Publicado: (2017)

A framework for predicting odor threshold values of perfumes by scientific machine learning and transfer learning
por: Oliveira, Luis M.C., et al.
Publicado: (2023)

Inverted Perceptual Judgment of Nociceptive Stimuli at Threshold Level following Inconsistent Cues
por: Walter, Carmen, et al.
Publicado: (2015)

Personalizing digital pain management with adapted machine learning approach
por: Fundoiano-Hershcovitz, Yifat, et al.
Publicado: (2023)

Machine-Learning Points at Endoscopic, Quality of Life, and Olfactory Parameters as Outcome Criteria for Endoscopic Paranasal Sinus Surgery in Chronic Rhinosinusitis
por: Lötsch, Jörn, et al.
Publicado: (2021)

Intrarater Reliability of Pain Intensity, Tissue Blood Flow, Thermal Pain Threshold, Pressure Pain Threshold and Lumbo-Pelvic Stability Tests in Subjects with Low Back Pain
por: Paungmali, Aatit, et al.
Publicado: (2012)

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data
por: Malkusch, Sebastian, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_vtf9o6rbkjel4243v0ea4u592q