Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction


Autoři: E. Tejera aff001;  I. Carrera aff003;  Karina Jimenes-Vargas aff001;  V. Armijos-Jaramillo aff001;  A. Sánchez-Rodríguez aff002;  M. Cruz-Monteagudo aff006;  Y. Perez-Castillo aff002
Působiště autorů: Ingeniería en Biotecnología, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador aff001;  Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador aff002;  Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, Ecuador aff003;  Departamento de Ciências de Computadores, Faculdade de Ciências, Universidade do Porto, Porto, Portugal aff004;  Universidad Técnica Particular de Loja, Loja, Ecuador aff005;  Center for Computational Science (CCS), University of Miami (UM), Miami, FL, United States of America aff006;  West Coast University, Miami, Florida, United States of America aff007;  Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, Ecuador aff008
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223276

Souhrn

The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.

Klíčová slova:

Algorithms – Database and informatics methods – Gene expression – Machine learning – Machine learning algorithms – Optimization – Support vector machines – Kernel functions


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 10

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