首页|Abdullah Gul University Reports Findings in Colon Cancer (CCPred: Global and pop ulation-specific colorectal cancer prediction and metagenomic biomarker identifi cation at different molecular levels using machine learning techniques)
Abdullah Gul University Reports Findings in Colon Cancer (CCPred: Global and pop ulation-specific colorectal cancer prediction and metagenomic biomarker identifi cation at different molecular levels using machine learning techniques)
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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 Kayseri, Turkey , by NewsRx correspondents, research stated, "Colorectal cancer (CRC) ranks as the third most common cancer globAlly and the second leading cause of cancer-rela ted deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression." Our news journalists obtained a quote from the research from Abdullah Gul Univer sity, "Understanding the complex interplay between disease development and metag enomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated wit h CRC, yet there is a need to improve their accuracy through a holistic biologic al knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and populati on-specific analyses. These analyses utilize relative abundance values from huma n gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and b iomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combi ned. Population-based analysis includes within-population, leaveone- dataset-out (LODO) and cross-population approaches. Four classification algorithms are empl oyed for CRC classification. Random Forest achieved an AUC of 0.83 for species d ata, 0.78 for enzyme data and 0.76 for pathway data globAlly. On the global scal e, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzy me biomarkers include RNA 2' 3' cyclic 3' phosphodiesterase; and pathway biomark ers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved d isease prediction and biomarker discovery."
KayseriTurkeyEurasiaBiomarkersCa ncerColon CancerColorectal ResearchCyborgsDiagnostics and ScreeningEme rging TechnologiesGastroenterologyHealth and MedicineMachine LearningOnc ology
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