2024
REPRINTED WITH PERMISSION OF IASP – PAIN 164 (2023): 1912–1926: Predicting chronic postsurgical pain: current evidence anda novel program to develop predictive biomarker signatures
Sluka K, Wager T, Sutherland S, Labosky P, Balach T, Bayman E, Berardi G, Brummett C, Burns J, Buvanendran A, Caffo B, Calhoun V, Clauw D, Chang A, Coffey C, Dailey D, Ecklund D, Fiehn O, Fisch K, Frey Law L, Harris R, Harte S, Howard T, Jacobs J, Jacobs J, Jepsen K, Johnston N, Langefeld C, Laurent L, Lenzi R, Lindquist M, Lokshin A, Kahn A, McCarthy R, Olivier M, Porter L, Qian W, Sankar C, Satterlee J, Swensen A, Vance C, Waljee J, Wandner L, Williams D, Wixson R, Zhou X. REPRINTED WITH PERMISSION OF IASP – PAIN 164 (2023): 1912–1926: Predicting chronic postsurgical pain: current evidence anda novel program to develop predictive biomarker signatures. Ból 2024, 25: 1-19. DOI: 10.5604/01.3001.0054.4396.Peer-Reviewed Original ResearchChronic painPain SignatureDevelopment of chronic painChronic postsurgical painChronification of painEvaluate candidate biomarkersAt-risk patientsInvestigation of biomarkersPredictive biomarker signaturesPostsurgical painTreat other diseasesBiological treatment targetPainCandidate biomarkersNovel biomarkersTreatment targetOther diseasesPhenotypic expressionBiomarker signaturesBiomarkersEarly interventionNational InstituteInitial findingsBiological pathwaysSurgery
2023
Mesothelioma: Peritoneal, Version 2.2023, NCCN Clinical Practice Guidelines in Oncology.
Ettinger D, Wood D, Stevenson J, Aisner D, Akerley W, Bauman J, Bharat A, Bruno D, Chang J, Chirieac L, DeCamp M, Dilling T, Dowell J, Durm G, Gettinger S, Grotz T, Gubens M, Hegde A, Lackner R, Lanuti M, Lin J, Loo B, Lovly C, Maldonado F, Massarelli E, Morgensztern D, Mullikin T, Ng T, Otterson G, Patel S, Patil T, Polanco P, Riely G, Riess J, Shapiro T, Singh A, Tam A, Tanvetyanon T, Yanagawa J, Yang S, Yau E, Gregory K, Hughes M. Mesothelioma: Peritoneal, Version 2.2023, NCCN Clinical Practice Guidelines in Oncology. Journal Of The National Comprehensive Cancer Network 2023, 21: 961-979. PMID: 37673108, DOI: 10.6004/jnccn.2023.0045.Peer-Reviewed Original Research
2018
Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding–Based Machine Learning Approach
Patrick M, Raja K, Miller K, Sotzen J, Gudjonsson J, Elder J, Tsoi L. Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding–Based Machine Learning Approach. Journal Of Investigative Dermatology 2018, 139: 683-691. PMID: 30342048, PMCID: PMC6387843, DOI: 10.1016/j.jid.2018.09.018.Peer-Reviewed Original ResearchConceptsImmune-mediated diseasesCutaneous diseaseEfficient bioinformatics approachBioinformatics approachDrug-disease relationshipsDrug repurposing approachChronic inflammatory condition of skinChronic inflammatory conditionsSequencing cohortPsoriatic lesional skinReceiver Operating CharacteristicRNA-sequencing cohortsClinical efficacyTreat other diseasesLesional skinAutoimmune diseasesRepurposing approachDrug repurposingCondition of skinOther diseasesDrugDisease
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