Exploring data distributions in machine learning models with SOMs
Data quality control is fundamental in data-driven analysis with machine learning (ML) models. In the domain of drug research, there is an increasing interest in the prediction of relevant biocompounds physicochemical properties with ML. In order to build predictive models of good quality, it is imp...
| Autores: | , |
|---|---|
| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/415031 |
| Acceso en línea: | https://hdl.handle.net/2117/415031 https://dx.doi.org/10.1007/978-3-031-67159-3_10 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Self-organizing maps ADME properties Random forest Interpretable machine learning Data quality Bioinformatics Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Data quality control is fundamental in data-driven analysis with machine learning (ML) models. In the domain of drug research, there is an increasing interest in the prediction of relevant biocompounds physicochemical properties with ML. In order to build predictive models of good quality, it is important to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage. |
|---|