BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

196 related articles for article (PubMed ID: 37333624)

  • 1. Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms.
    Xiong J; Chen G; Liu Z; Wu X; Xu S; Xiong J; Ji S; Wu M
    Precis Clin Med; 2023 Jun; 6(2):pbad009. PubMed ID: 37333624
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Identifying immuno-related diagnostic genes and immune infiltration signatures for periodontitis and alopecia areata.
    Wang H; Wei R; Deng T; Zhang J; Shen Z
    Int Immunopharmacol; 2023 Nov; 124(Pt B):110880. PubMed ID: 37717318
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of the Risk of Alopecia Areata Progressing to Alopecia Totalis and Alopecia Universalis: Biomarker Development with Bioinformatics Analysis and Machine Learning.
    Zhang T; Nie Y
    Dermatology; 2022; 238(2):386-396. PubMed ID: 34004600
    [TBL] [Abstract][Full Text] [Related]  

  • 4. CCL13 is upregulated in alopecia areata lesions and is correlated with disease severity.
    Wang D; Xu X; Li X; Shi J; Tong X; Chen J; Lu J; Huang J; Yang S
    Exp Dermatol; 2021 May; 30(5):723-732. PubMed ID: 33523560
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification and verification of EOMEs regulated network in Alopecia areata.
    Yuan X; Tang Y; Zhao Z; Liu F; Shi W; Zhang Y; Li J
    Int Immunopharmacol; 2020 Jul; 84():106544. PubMed ID: 32353685
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning.
    Xu W; Wei D; Song X
    Sci Rep; 2024 Feb; 14(1):3800. PubMed ID: 38360836
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identification of blood microRNA alterations in patients with severe active alopecia areata.
    Sheng Y; Qi S; Hu R; Zhao J; Rui W; Miao Y; Ma J; Yang Q
    J Cell Biochem; 2019 Sep; 120(9):14421-14430. PubMed ID: 30983035
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity.
    Karami H; Nomiri S; Ghasemigol M; Mehrvarzian N; Derakhshani A; Fereidouni M; Mirimoghaddam M; Safarpour H
    IET Syst Biol; 2022 Sep; 16(5):173-185. PubMed ID: 35983595
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bioinformatics analysis of genes associated with the patchy-type alopecia areata: CD2 may be a new therapeutic target.
    Shi J; Peng P; Liu W; Mi P; Xing C; Ning G; Feng S
    Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub; 2020 Dec; 164(4):380-386. PubMed ID: 31558844
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Immune-associated pivotal biomarkers identification and competing endogenous RNA network construction in post-operative atrial fibrillation by comprehensive bioinformatics and machine learning strategies.
    Zhou Y; Wu Q; Ni G; Hong Y; Xiao S; Liu C; Yu Z
    Front Immunol; 2022; 13():974935. PubMed ID: 36341343
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Bioinformatics analysis of differentially expressed genes on alopecia.
    Xiang H; Yang XH; Ai LX; Pan YP; Hu Y
    Yi Chuan; 2020 Feb; 42(2):172-182. PubMed ID: 32102774
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Pathogenesis of Alopecia Areata Based on Bioinformatics Analysis.
    Zhang Z; Wang X; Zhang R
    Indian J Dermatol; 2019; 64(1):1-6. PubMed ID: 30745627
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning.
    Zhang Q; Guo Y; Zhang B; Liu H; Peng Y; Wang D; Zhang D
    Front Cardiovasc Med; 2022; 9():939972. PubMed ID: 35958412
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of Immune-Associated Genes in Diagnosing Aortic Valve Calcification With Metabolic Syndrome by Integrated Bioinformatics Analysis and Machine Learning.
    Zhou Y; Shi W; Zhao D; Xiao S; Wang K; Wang J
    Front Immunol; 2022; 13():937886. PubMed ID: 35865542
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis.
    Liu C; Tang L; Zhou Y; Tang X; Zhang G; Zhu Q; Zhou Y
    Eur J Med Res; 2023 Feb; 28(1):92. PubMed ID: 36823662
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Differential expression patterns of specific long noncoding RNAs and competing endogenous RNA network in alopecia areata.
    Sheng Y; Ma J; Zhao J; Qi S; Hu R; Yang Q
    J Cell Biochem; 2019 Jun; 120(6):10737-10747. PubMed ID: 30790320
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.
    Liu J; Liu L; Antwi PA; Luo Y; Liang F
    Front Genet; 2022; 13():858466. PubMed ID: 35719392
    [No Abstract]   [Full Text] [Related]  

  • 18. The current state of knowledge of the immune ecosystem in alopecia areata.
    Connell SJ; Jabbari A
    Autoimmun Rev; 2022 May; 21(5):103061. PubMed ID: 35151885
    [TBL] [Abstract][Full Text] [Related]  

  • 19. WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy.
    Kong X; Sun H; Wei K; Meng L; Lv X; Liu C; Lin F; Gu X
    Front Cardiovasc Med; 2023; 10():1058834. PubMed ID: 37008314
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Peripheral blood gene expression in alopecia areata reveals molecular pathways distinguishing heritability, disease and severity.
    Coda AB; Qafalijaj Hysa V; Seiffert-Sinha K; Sinha AA
    Genes Immun; 2010 Oct; 11(7):531-41. PubMed ID: 20535136
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.