首页|University of Tirana Reports Findings in Colon Cancer (Robust prediction of colo rectal cancer via gut microbiome 16S rRNA sequencing data)
University of Tirana Reports Findings in Colon Cancer (Robust prediction of colo rectal cancer via gut microbiome 16S rRNA sequencing data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Colon Cance r is the subject of a report. According to news originating from Tirana, Albania , by NewsRx correspondents, research stated, “The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal can cer (CRC). The research emphasizes the potential of using machine learning techn iques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.” Our news journalists obtained a quote from the research from the University of T irana, “The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classic al statistical models in high-dimensional settings. The primary objective of thi s study is to explore and validate the potential of the human microbiome, specif ically in the colon, as a valuable source of biomarkers for colorectal cancer (C RC) detection and progression. The focus is on developing a classifier that effe ctively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets. To achieve the aim, various machine learning techniques are employed, including random forest ( RF), recursive feature elimination (RFE) and a robust correlation-based techniqu e known as the fuzzy forest (FF). The study utilizes these methods to analyse th e three datasets, comparing their performance in predicting CRC and normal sampl es. The emphasis is on identifying the most relevant microbial features (taxa) a ssociated with CRC development via partial dependence plots, i.e. a machine lear ning tool focused on explainability, visualizing how a feature influences the pr edicted outcome. The analysis of the three faecal 16S rRNA sequencing datasets r eveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation probl em when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-in vasive means to detect CRC and underscore the significance of employing FF for i mproved predictive accuracy.”
TiranaAlbaniaBiomarkersCancerCol on CancerColorectal ResearchCyborgsDiagnostics and ScreeningEmerging Tec hnologiesGastroenterologyHealth and MedicineMachine LearningOncology
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