Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.
2021Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain
Kringel, D.; Malkusch, S.; Kalso, E.; Lötsch, J.
Journal Article
2021Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis
Kringel, D.; Malkusch, S.; Lötsch, J.
Journal Article
2020Machine-learned association of next-generation sequencing-derived variants in thermosensitive ion channels genes with human thermal pain sensitivity phenotypes
Lötsch, J.; Kringel, D.; Geisslinger, G.; Oertel, B.G.; Resch, E.; Malkusch, S.
Journal Article
2019Machine Learning in Human Olfactory Research
Lötsch, J.; Kringel, D.; Hummel, T.
Journal Article
2019Machine-learned analysis of global and glial/opioid intersection-related DNA methylation in patients with persistent pain after breast cancer surgery
Kringel, D.; Kaunisto, M.A.; Kalso, E.; Lötsch, J.
Journal Article
2019Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery
Kringel, D.; Kaunisto, M.A.; Kalso, E.; Lötsch, J.
Journal Article
2018Computational functional genomics-based approaches in analgesic drug discovery and repurposing
Lippmann, C.; Kringel, D.; Ultsch, A.; Lötsch, J.
Journal Article
2018Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain
Kringel, D.; Kaunisto, M.A.; Lippmann, C.; Kalso, E.; Lötsch, J.
Journal Article
2018A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes
Kringel, D.; Lippmann, C.; Parnham, M.J.; Kalso, E.; Ultsch, A.; Lötsch, J.
Journal Article
2018Machine-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
Kringel, D.; Geisslinger, G.; Resch, E.; Oertel, B.G.; Thrun, M.C.; Heinemann, S.; Lötsch, J.
Journal Article
2018Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
Lötsch, J.; Sipilä, R.; Tasmuth, T.; Kringel, D.; Estlander, A.-M.; Meretoja, T.; Kalso, E.; Ultsch, A.
Journal Article
2018Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications
Lötsch, J.; Kringel, D.
Journal Article
2017Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses
Kringel, D.; Ultsch, A.; Zimmermann, M.; Jansen, J.P.; Ilias, W.; Freynhagen, R.; Griessinger, N.; Kopf, A.; Stein, C.; Doehring, A.; Resch, E.; Lötsch, J.
Journal Article
2017Integrated computational analysis of genes associated with human hereditary insensitivity to pain
Lötsch, J.; Lippmann, C.; Kringel, D.; Ultsch, A.
Journal Article
2017Next-generation sequencing of the human TRPV1 gene and the regulating co-players LTB4R and LTB4R2 based on a custom AmpliSeq (TM) panel
Kringel, D.; Sisignano, M.; Zinn, S.; Lötsch, J.
Journal Article
2016A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity
Ultsch, A.; Kringel, D.; Kalso, E.; Mogil, J.S.; Lötsch, J.
Journal Article
2016Next-generation sequencing of human opioid receptor genes based on a custom AmpliSeq™ library and ion torrent personal genome machine
Kringel, D.; Lötsch, J.
Journal Article
2015Pain research funding by the European Union Seventh Framework Programme
Kringel, D.; Lötsch, J.
Journal Article