191 related articles for article (PubMed ID: 37266435)
1. Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia.
Liang H; Kong X; Cao Z; Wang H; Liu E; Sun F; Qi J; Zhang Q; Zhou Y
Front Immunol; 2023; 14():1194353. PubMed ID: 37266435
[TBL] [Abstract][Full Text] [Related]
2. Identification of the key genes and microRNAs in adult acute myeloid leukemia with FLT3 mutation by bioinformatics analysis.
Chen S; Chen Y; Zhu Z; Tan H; Lu J; Qin P; Xu L
Int J Med Sci; 2020; 17(9):1269-1280. PubMed ID: 32547322
[No Abstract] [Full Text] [Related]
3. MicroRNA-363-3p promote the development of acute myeloid leukemia with RUNX1 mutation by targeting SPRYD4 and FNDC3B.
Chen Y; Chen S; Lu J; Yuan D; He L; Qin P; Tan H; Xu L
Medicine (Baltimore); 2021 May; 100(18):e25807. PubMed ID: 33950983
[TBL] [Abstract][Full Text] [Related]
4. Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses.
Ren Y; Liang H; Huang Y; Miao Y; Li R; Qiang J; Wu L; Qi J; Li Y; Xia Y; Huang L; Wang S; Kong X; Zhou Y; Zhang Q; Zhu G
Front Immunol; 2024; 15():1341255. PubMed ID: 38464517
[TBL] [Abstract][Full Text] [Related]
5. Identification of new markers discriminating between myeloid and lymphoid acute leukemia.
Haouas H; Haouas S; Uzan G; Hafsia A
Hematology; 2010 Aug; 15(4):193-203. PubMed ID: 20670477
[TBL] [Abstract][Full Text] [Related]
6. Exploring Prognostic Biomarkers of Acute Myeloid Leukemia to Determine Its Most Effective Drugs from the FDA-Approved List through Molecular Docking and Dynamic Simulation.
Alom MM; Faruqe MO; Molla MKI; Rahman MM
Biomed Res Int; 2023; 2023():1946703. PubMed ID: 37359050
[TBL] [Abstract][Full Text] [Related]
7. Bioinformatics Analysis Identifies Key Genes and Pathways in Acute Myeloid Leukemia Associated with DNMT3A Mutation.
Chen S; Chen Y; Lu J; Yuan D; He L; Tan H; Xu L
Biomed Res Int; 2020; 2020():9321630. PubMed ID: 33299888
[TBL] [Abstract][Full Text] [Related]
8. Identification of Key Genes and Pathways Associated with RUNX1 Mutations in Acute Myeloid Leukemia Using Bioinformatics Analysis.
Zhu F; Huang R; Li J; Liao X; Huang Y; Lai Y
Med Sci Monit; 2018 Oct; 24():7100-7108. PubMed ID: 30289875
[TBL] [Abstract][Full Text] [Related]
9. Identification of intrinsically disordered regions in hub genes of acute myeloid leukemia: A bioinformatics approach.
Ameri M; Alipour M; Madihi M; Nezafat N
Biotechnol Appl Biochem; 2022 Dec; 69(6):2304-2322. PubMed ID: 34812529
[TBL] [Abstract][Full Text] [Related]
10. Identification of potential therapeutic target genes, key miRNAs and mechanisms in acute myeloid leukemia based on bioinformatics analysis.
Zhao Y; Zhang X; Zhao Y; Kong D; Qin F; Sun J; Dong Y
Med Oncol; 2015 May; 32(5):152. PubMed ID: 25832863
[TBL] [Abstract][Full Text] [Related]
11. Integrated bioinformatic analysis of mitochondrial metabolism-related genes in acute myeloid leukemia.
Tong X; Zhou F
Front Immunol; 2023; 14():1120670. PubMed ID: 37138869
[TBL] [Abstract][Full Text] [Related]
12. Identification of prognostic genes in the acute myeloid leukemia immune microenvironment based on TCGA data analysis.
Yan H; Qu J; Cao W; Liu Y; Zheng G; Zhang E; Cai Z
Cancer Immunol Immunother; 2019 Dec; 68(12):1971-1978. PubMed ID: 31650199
[TBL] [Abstract][Full Text] [Related]
13. Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma.
Zhou L; Tang H; Wang F; Chen L; Ou S; Wu T; Xu J; Guo K
Mol Med Rep; 2018 Nov; 18(5):4185-4196. PubMed ID: 30132538
[TBL] [Abstract][Full Text] [Related]
14. The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.
Zhang C; Peng L; Zhang Y; Liu Z; Li W; Chen S; Li G
Med Oncol; 2017 Jun; 34(6):101. PubMed ID: 28432618
[TBL] [Abstract][Full Text] [Related]
15. Bioinformatics analysis of gene expression profiles in childhood B-precursor acute lymphoblastic leukemia.
Li J; Zhai X; Wang H; Qian X; Miao H; Zhu X
Hematology; 2015 Aug; 20(7):377-83. PubMed ID: 25431969
[TBL] [Abstract][Full Text] [Related]
16. Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis.
Wen P; Chidanguro T; Shi Z; Gu H; Wang N; Wang T; Li Y; Gao J
Mol Med Rep; 2018 Aug; 18(2):1538-1550. PubMed ID: 29845250
[TBL] [Abstract][Full Text] [Related]
17. Identification of Key Pathways and Genes in Anaplastic Thyroid Carcinoma via Integrated Bioinformatics Analysis.
Hu S; Liao Y; Chen L
Med Sci Monit; 2018 Sep; 24():6438-6448. PubMed ID: 30213925
[TBL] [Abstract][Full Text] [Related]
18. Screening and
Fard AA; Rahimi H; Shams Z; Ghoraeian P
Microrna; 2022; 11(3):227-244. PubMed ID: 35546758
[TBL] [Abstract][Full Text] [Related]
19. [Prognostic implications and functional enrichment analysis of LTB4R in patients with acute myeloid leukemia].
Zhang X; Zhang X; Liu P; Liu K; Li W; Chen Q; Ma W
Nan Fang Yi Ke Da Xue Xue Bao; 2022 Mar; 42(3):309-320. PubMed ID: 35426793
[TBL] [Abstract][Full Text] [Related]
20. Construction of prognostic risk prediction model based on high-throughput sequencing expression profile data in childhood acute myeloid leukemia.
Niu P; Yao B; Wei L; Zhu H; Fang C; Zhao Y
Blood Cells Mol Dis; 2019 Jul; 77():43-50. PubMed ID: 30954792
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
