4 research outputs found
A machine learning pipeline for discriminant pathways identification
Motivation: Identifying the molecular pathways more prone to disruption
during a pathological process is a key task in network medicine and, more in
general, in systems biology.
Results: In this work we propose a pipeline that couples a machine learning
solution for molecular profiling with a recent network comparison method. The
pipeline can identify changes occurring between specific sub-modules of
networks built in a case-control biomarker study, discriminating key groups of
genes whose interactions are modified by an underlying condition. The proposal
is independent from the classification algorithm used. Three applications on
genomewide data are presented regarding children susceptibility to air
pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's.
Availability: Details about the software used for the experiments discussed
in this paper are provided in the Appendix
A machine learning pipeline for quantitative phenotype prediction from genotype data
<p>Abstract</p> <p>Background</p> <p>Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.</p> <p>Methods</p> <p>The core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected.</p> <p>Results</p> <p>With respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms.</p> <p>Conclusions</p> <p>The L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.</p
Algebraic Comparison of Partial Lists in Bioinformatics
The outcome of a functional genomics pipeline is usually a partial list of
genomic features, ranked by their relevance in modelling biological phenotype
in terms of a classification or regression model. Due to resampling protocols
or just within a meta-analysis comparison, instead of one list it is often the
case that sets of alternative feature lists (possibly of different lengths) are
obtained. Here we introduce a method, based on the algebraic theory of
symmetric groups, for studying the variability between lists ("list stability")
in the case of lists of unequal length. We provide algorithms evaluating
stability for lists embedded in the full feature set or just limited to the
features occurring in the partial lists. The method is demonstrated first on
synthetic data in a gene filtering task and then for finding gene profiles on a
recent prostate cancer dataset