BackgroundHere, we present an R package for entropy/variability analysis that facilitates prompt and convenient data extraction, manipulation and visualization of protein features from multiple sequence alignments. BALCONY can work with residues dispersed across a protein sequence and map them on the corresponding alignment of homologous protein sequences. Additionally, it provides several entropy and variability scores that indicate the conservation of each residue.ResultsOur package allows the user to visualize evolutionary variability by locating the positions most likely to vary and to assess mutation candidates in protein engineering.ConclusionIn comparison to other R packages BALCONY allows conservation/variability analysis in context of protein structure with linkage of the appropriate metrics with physicochemical features of user choice.Availability: CRAN project page: https://cran.r-project.org/package=BALCONY and our website: http://www.tunnelinggroup.pl/software/ for major platforms: Linux/Unix, Windows and Mac OS X.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2294-z) contains supplementary material, which is available to authorized users.
(1) Background: The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. (2) Methods: We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. (3) Results: We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). (4) Conclusions: Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.
The use of machine learning has increased over the years, especially in the world of molecular data. Generally, the inference of relationships between features is determined by statistical models. The phenotype (observable clinical characteristics) can result from the expression of the genotype (genetic code) or environmental factors. Molecular datasets have limited information, while supporting clinical data is ambiguous. There are no well-established approaches for combining clinical information with genomic repositories. The genomic tests that are available only use molecular data and give physicians a result which can be integrated clinically. In this paper, we present the strategy where clinical data, regardless of its limitations, is combined in one predictive model with molecular features. We predict the risk of malignancy in the thyroid nodules based on the results of fine-needle aspiration biopsy and expression of selected genes. We utilize a Bayesian network (BN) framework to discover relationships between molecular features and assess the impact of added clinical data quality on the performance of the chosen gene set. Bayesian network offering both prognostic and diagnostic perspectives is a perfect non-parametric technique for feature selection, feature extraction, and prediction purposes. We show that certain clinical factors could work as a synthetic feature and provide predictive abilities beyond what genes alone can offer. The experimental results demonstrate a higher performance of predictive models based on molecular and clinical data than when using only molecular data. We also explain why, one should consider the source of clinical data, but be aware of the quality of variables.
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