222 related articles for article (PubMed ID: 38069266)
41. Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.
Liu Y; Song C; Tian Z; Shen W
Int J Gen Med; 2023; 16():1909-1925. PubMed ID: 37228741
[TBL] [Abstract][Full Text] [Related]
42. Comprehensive Analyses Identify APOBEC3A as a Genomic Instability-Associated Immune Prognostic Biomarker in Ovarian Cancer.
Xu F; Liu T; Zhou Z; Zou C; Xu S
Front Immunol; 2021; 12():749369. PubMed ID: 34745121
[TBL] [Abstract][Full Text] [Related]
43. Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods.
Chen ZA; Ma HH; Wang Y; Tian H; Mi JW; Yao DM; Yang CJ
Sci Rep; 2023 Feb; 13(1):2694. PubMed ID: 36792688
[TBL] [Abstract][Full Text] [Related]
44. CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer.
Hua T; Liu DX; Zhang XC; Li ST; Yan P; Zhao Q; Chen SB
Front Immunol; 2023; 14():1151109. PubMed ID: 37063862
[TBL] [Abstract][Full Text] [Related]
45. Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.
Yun K; He T; Zhen S; Quan M; Yang X; Man D; Zhang S; Wang W; Han X
J Transl Med; 2023 May; 21(1):353. PubMed ID: 37246225
[TBL] [Abstract][Full Text] [Related]
46. Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma.
Ma J; Bo Z; Zhao Z; Yang J; Yang Y; Li H; Yang Y; Wang J; Su Q; Wang J; Chen K; Yu Z; Wang Y; Chen G
Cancers (Basel); 2023 Jan; 15(3):. PubMed ID: 36765583
[TBL] [Abstract][Full Text] [Related]
47. Immune-related gene signature to predict TACE refractoriness in patients with hepatocellular carcinoma based on artificial neural network.
Xu Q; Wang C; Yin G
Front Genet; 2022; 13():993509. PubMed ID: 36685822
[No Abstract] [Full Text] [Related]
48. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.
Zhang Y; Yang D; Liu Z; Chen C; Ge M; Li X; Luo T; Wu Z; Shi C; Wang B; Huang X; Zhang X; Zhou S; Hei Z
J Transl Med; 2021 Jul; 19(1):321. PubMed ID: 34321016
[TBL] [Abstract][Full Text] [Related]
49. Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.
Bifarin OO; Fernández FM
J Am Soc Mass Spectrom; 2024 Jun; 35(6):1089-1100. PubMed ID: 38690775
[TBL] [Abstract][Full Text] [Related]
50. Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases.
Cai D; Xiao T; Zou A; Mao L; Chi B; Wang Y; Wang Q; Ji Y; Sun L
Front Cardiovasc Med; 2022; 9():964894. PubMed ID: 36158815
[TBL] [Abstract][Full Text] [Related]
51. Screening ovarian cancer by using risk factors: machine learning assists.
Nopour R
Biomed Eng Online; 2024 Feb; 23(1):18. PubMed ID: 38347611
[TBL] [Abstract][Full Text] [Related]
52. Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer.
Laios A; Kalampokis E; Johnson R; Munot S; Thangavelu A; Hutson R; Broadhead T; Theophilou G; Leach C; Nugent D; De Jong D
Cancers (Basel); 2022 Jul; 14(14):. PubMed ID: 35884506
[TBL] [Abstract][Full Text] [Related]
53. A 5-year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms.
Yu W; Lu Y; Shou H; Xu H; Shi L; Geng X; Song T
Cancer Med; 2023 Mar; 12(6):6867-6876. PubMed ID: 36479910
[TBL] [Abstract][Full Text] [Related]
54. A novel biomarker, MRPS12 functions as a potential oncogene in ovarian cancer and is a promising prognostic candidate.
Qiu X; Guo D; Du J; Bai Y; Wang F
Medicine (Baltimore); 2021 Feb; 100(8):e24898. PubMed ID: 33663122
[TBL] [Abstract][Full Text] [Related]
55. Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears.
Li C; Alike Y; Hou J; Long Y; Zheng Z; Meng K; Yang R
Knee Surg Sports Traumatol Arthrosc; 2023 Jul; 31(7):2615-2623. PubMed ID: 36629889
[TBL] [Abstract][Full Text] [Related]
56. Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing.
Zhao S; Ye B; Chi H; Cheng C; Liu J
Heliyon; 2023 Jul; 9(7):e17454. PubMed ID: 37449151
[TBL] [Abstract][Full Text] [Related]
57. Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models.
Choi H; Kim JH; Kim H; Cheon KA
Front Neurosci; 2023; 17():1229155. PubMed ID: 37706158
[TBL] [Abstract][Full Text] [Related]
58. Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer.
Gong X; Zheng B; Xu G; Chen H; Chen C
J Thorac Dis; 2021 Nov; 13(11):6240-6251. PubMed ID: 34992804
[TBL] [Abstract][Full Text] [Related]
59. Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis.
Wu Z; Chen H; Ke S; Mo L; Qiu M; Zhu G; Zhu W; Liu L
Sci Rep; 2023 Oct; 13(1):16559. PubMed ID: 37783761
[TBL] [Abstract][Full Text] [Related]
60. Identification of novel characteristic biomarkers and immune infiltration profile for the anaplastic thyroid cancer via machine learning algorithms.
Li C; Dong X; Yuan Q; Xu G; Di Z; Yang Y; Hou J; Zheng L; Chen W; Wu G
J Endocrinol Invest; 2023 Aug; 46(8):1633-1650. PubMed ID: 36725810
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
