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1 Introduction Esophageal cancer (OC) is currently the eighth most common form of cancer worldwide [1]. The incidence rate is rapidly increasing, with a sixfold increase reported in recent years [2]. OC primarily includes two main subtypes: squamous cell carcinoma (OSCC) and adenocarcinoma (OAC), each with distinct molecular characteristics, risk factors, and clinical outcomes [3].
Abstract Background: Inflammatory bowel diseases (IBDs) are chronic inflammatory disorders with a dysregulated immune response partly influenced by environmental factors. DNA methylation (DNAm), a key epigenetic mechanism, is implicated in the etiology of complex diseases, including IBD. Epigenetic clocks, which use DNAm patterns to estimate biological aging, have been increasingly linked to various health and disease states.
A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator Trevor Doherty, Emma Dempster, Eilis Hannon, Jonathan Mill, Richie Poulton, David Corcoran, Karen Sugden, Benjamin Williams, Avshalom Caspi, Terrie E Moffitt, Sarah Jane Delany, Therese M Murphy bioRxiv 2022.04.02.486242; doi: https://doi.org/10.1101/2022.04.02.486242
Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer.
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