AIMC Topic: Biomarkers, Tumor

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Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Molecular cancer
Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern ...

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BMC medicine
BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high di...

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

Journal of proteome research
The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of machine learning algorithms in diagnosing and predicting the prognosis for PCNSL based on cerebrospinal...

Machine learning-based identification of cuproptosis-related lncRNA biomarkers in diffuse large B-cell lymphoma.

Cell biology and toxicology
Multiple machine learning techniques were employed to identify key long non-coding RNA (lncRNA) biomarkers associated with cuproptosis in Diffuse Large B-Cell Lymphoma (DLBCL). Data from the TCGA and GEO databases facilitated the identification of 12...

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.

Breast cancer research : BCR
BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated pro...

Surface-Enhanced Raman Scattering (SERS) combined with machine learning enables accurate diagnosis of cervical cancer: From molecule to cell to tissue level.

Critical reviews in oncology/hematology
The rising number of cervical cancer cases is placing a heavy economic strain on the country and its people. Improving survival rates hinges on early detection, precise diagnosis, and thorough treatment. Common screening and diagnostic methods like P...

Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. Th...