Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Applying Machine Learning To Biological Status (Qvalues) From Physio-Chemical Conditions Of Irish Rivers, Raúl Martín Sánchez
Applying Machine Learning To Biological Status (Qvalues) From Physio-Chemical Conditions Of Irish Rivers, Raúl Martín Sánchez
ICT
This thesis evaluates and optimises a variety of predictive models for assessing biological classification status, with an emphasis on water quality monitoring. Grounded in previous pertinent studies, it builds on the findings of (Arrighi and Castelli, 2023) concerning Tuscany’s river catchments, highlighting a solid correlation between river ecological status and parameters like summer climate and land use. They achieved an 80% prediction precision using the Random Forest algorithm, particularly adept at identifying "good" ecological conditions, leveraging a dataset devoid of chemical data.
Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo
Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo
ICT
Colorectal Cancer (CRC) is a globally prevalent and deadly carcinoma, necessitating advanced treatment approaches. Despite ongoing advancements, the mortality rate remains high. Various biological models, including animal studies, cell lines, and the emerging organoid model, contribute to understanding molecular mechanisms. Organoids, 3D cultures derived from tumor epithelial cells, offer advantages such as enhanced diversity, genetic modification, and extended culture capabilities. Recent applications of machine learning (ML) in predicting CRC treatment responses using organoids and tissue data indicate a promising avenue for advancing personalized therapies.