Using random forests for assistance in the curation of G-protein coupled receptor databases

Background: Biology is experiencing a gradual but fast transformation from a laboratory-centred science towards a data-centred one. As such, it requires robust data engineering and the use of quantitative data analysis methods as part of database curation. This paper focuses on G protein-coupled receptors, a large and heterogeneous super-family of cell membrane proteins of interest to biology in general. One of its families, Class C, is of particular interest to pharmacology and drug design. This family is quite heterogeneous on its own, and the discrimination of its several sub-families is a challenging problem. In the absence of known crystal structure, such discrimination must rely on their primary amino acid sequences. Methods: We are interested not as much in achieving maximum sub-family discrimination accuracy using quantitative methods, but in exploring sequence misclassification behavior. Specifically, we are interested in isolating those sequences showing consistent misclassification, that is, sequences that are very often misclassified and almost always to the same wrong sub-family. Random forests are used for this analysis due to their ensemble nature, which makes them naturally suited to gauge the consistency of misclassification. This consistency is here defined through the voting scheme of their base tree classifiers. Results: Detailed consistency results for the random forest ensemble classification were obtained for all receptors and for all data transformations of their unaligned primary sequences. Shortlists of the most consistently misclassified receptors for each subfamily and transformation, as well as an overall shortlist including those cases that were consistently misclassified across transformations, were obtained. The latter should be referred to experts for further investigation as a data curation task. Conclusion: The automatic discrimination of the Class C sub-families of G protein-coupled receptors from their unaligned primary sequences shows clear limits. This study has investigated in some detail the consistency of their misclassification using random forest ensemble classifiers. Different sub-families have been shown to display very different discrimination consistency behaviors. The individual identification of consistently misclassified sequences should provide a tool for quality control to GPCR database curators.Peer ReviewedPostprint (published version

Water Quality Analysis Using Machine Learning Algorithms

Data analysis is one of the key engines of progress in most of areas of the research in natural sciences, including environmental sciences. Nowadays, continuous development and technological progress provide us with universal and advanced tools for data analysis, such as machine learning algorithms. The purpose of the research behind this thesis is to provide examples to the engineers and scientists working in environmental field of how these models can be implemented towards the environmental tasks. The data used for the research is water quality data, produced during the STREAMES (STream REAch Management, an Expert System) project, initially aiming at producing tools for increasing the quality of European rivers. In this report one will find examples of data imputation, regression, classification, clusterization and feature selection tasks using machine learning algorithms, such as: random forest, support vector machines, neural networks, k-nearest neighbours, and k-means clustering

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Additional file 2. Additional tables for Class C GPCR sub-families CS, VN, Od and Ta. They provide the same detailed information asTables 7, 8, 9, 10 and 11 for the remaining Class C GPCR sub-families CS, VN, Od and Ta

MOESM1 of Using random forests for assistance in the curation of G-protein coupled receptor databases

Additional file 1. Additional figures for Class C GPCR sub-families CS, VN, Od and Ta. They provide the same information concerningsequence-specific consistencies as Figs. 2, 3, 4, 5, 6, 7, 8, 9 and 10 for the remaining Class C GPCR sub-families CS, VN, Od and Ta

Systematic genetics and single‐cell imaging reveal widespread morphological pleiotropy and cell‐to‐cell variability

Abstract Our ability to understand the genotype‐to‐phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single‐cell level. To systematically assess cell‐to‐cell phenotypic variability, we combined automated yeast genetics, high‐content screening and neural network‐based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments—endocytic patch, actin patch, late endosome and vacuole—identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single‐cell level. Our single‐cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress