Jorn Lotsch

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Professor (Full)

Goethe University Frankfurt

Germany

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Professor Jorn Lotsch is a full professor at Goethe-University in Germany. His research areas include the integration of AI and machine learning in pain research and therapy, as well as lipidomics and genetics related to human pain. Recent publications highlight his work on machine learning applications in clinical pharmacology and the biological validation of potential mediators in neuropathy. His contributions also examine the long-term effects of COVID-19 on olfactory function and the clinical assessment of this sensory modality.

Recent Grants

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Generative artificial intelligence based algorithm to increase the predictivity of preclinical studies while keeping sample sizes small

Open Date: 2022-01-01

Close Date: 2025-12-01

Grant: Close

Reproducible cleaning of biomedical laboratory data using methods of visualization, error correction and transformation implemented as interactive R-notebooks

Open Date: 2018-10-01

Close Date: 2020-12-01

Grant: Close

Noninvasive central modulation of pain follwing TMS inhibition of the secondary somatosensory cortex in healthy subjects

Open Date: 2013-01-01

Close Date: 2015-12-31

Grant: Close

Doktorandenkolleg für translationale Arzneimittelforschung

Open Date: 2012-08-01

Close Date: 2018-08-01

Grant: Close

Fraunhofer-Projektgruppe "Translationale Medizin und Pharmakologie TMP"

Open Date: 2012-01-01

Close Date: 2017-12-01

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Collaborators (8)

Charlotte Pretzsch

King’s College London

UNITED KINGDOM

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Alfred Ultsch

University of Marburg

GERMANY

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Eija Kalso

University of Helsinki

FINLAND

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Teemu A Zetterman

University of Helsinki

FINLAND

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Anni Irmeli Nieminen

University of Helsinki

FINLAND

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Thomas Hummel

Technische Universität Dresden

GERMANY

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Sagnik Bhattacharyya

King’s College London

UNITED KINGDOM

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Mattia Veronese

King’s College London

UNITED KINGDOM

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Jorn Lotsch

Research Interests

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About

Recent Grants

Articles (24)

Integrating AI and Machine Learning Into Pain Research and Therapy

Targeted lipidomics dataset of central nervous system and plasma from mice with experimental autoimmune encephalomyelitis

Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing.

Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism

Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods

Artificial intelligence and machine learning in clinical pharmacological research

Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning

Odor dilution sorting as a clinical test of olfactory function: normative values and reliability data.

Comparative assessment of projection and clustering method combinations in the analysis of biomedical data

Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks

Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients.

Comments on the importance of visualizing the distribution of pain‐related data

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

Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size.

Pitfalls of Using Multinomial Regression Analysis to Identify Class-Structure-Relevant Variables in Biomedical Data Sets: Why a Mixture of Experts (MOE) Approach Is Better

Machine learning identifies right index finger tenderness as key signal of DAS28-CRP based psoriatic arthritis activity.

A perspective of randomness in a clinical test of olfactory performance.

Task-independent acute effects of delta-9-tetrahydrocannabinol on human brain function and its relationship with cannabinoid receptor gene expression

Enhancing Explainable Machine Learning by Reconsidering Initially Unselected Items in Feature Selection for Classification

Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data

Artificial intelligence and machine learning in pain research: a data scientometric analysis.

Pharmacological data science perspective on fatal incidents of morphine treatment.

A Biomedical Case Study Showing That Tuning Random Forests Can Fundamentally Change the Interpretation of Supervised Data Structure Exploration Aimed at Knowledge Discovery

Sorting of Odor Dilutions Is a Meaningful Addition to Assessments of Olfactory Function as Suggested by Machine-Learning-Based Analyses

Collaborators (8)

Charlotte Pretzsch

Alfred Ultsch

Eija Kalso

Teemu A Zetterman

Anni Irmeli Nieminen

Thomas Hummel

Sagnik Bhattacharyya

Mattia Veronese

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