BIOINFORMATICS REVIEW

2005

Citations in the corpus

• Interpreting Classifiers through Attribute Interactions in Datasets (2017, background): “, 2017; Cheng & Zhao, 2014) and investigating associations between single nucleotide polymorphisms (SNPs) in bioinformatics (e.g., Lunetta et al., 2004; Moore et al., 2010).”
• Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing (2013, background): “Genetic programming and its many variations have been applied successfully in a wide range of different problem domains including bioinformatics (Fogel and Corne 2003) and genetic analysis (Moore et al. 2010).”
• Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, background): “epistasis) is a significant challenge driving bioinformatic development [1-4].”
• Visualizing GO Annotations. (2016, background): “Do these genes participate in the same metabolic or signaling pathways? Do they perform similar biochemical functions? Do their protein products co-localize in the cell? Formally, such sets of genes are subjected to statistical tests for enrichment for various functional categories (2).”
• Discovering causal interactions using Bayesian network scoring and information gain (2016, background): “It is believed that much of genetic risk for disease is due to epistatic interactions [6 – 9].”
• Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, background): “ANDVARIABLE INTERACTIONS For data sets generated in the Life Sciences, e.g. for metabolomics and proteomics measurements, gene expression data and GWAS studies, variables (e.g. SNPs in genetic association studies) are typically important for a subset of samples of the same class (e.g. patients) and ”
• A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “As these methods have been comprehensively described elsewhere [27,30–41], here we shall avoid in-depth and complex descriptions of the mathematics behind these classification models and instead provide simple explanations which include both the advantages and caveats as well as practical applicatio”
• Identification of a molecular signaling network that regulates a cellular necrotic cell death pathway. (2008, methodology): “…categories for the hit genes relative to their representation in the global set of genes examined in the siRNA screen, p values were computed using the hypergeometric test (Rivals et al., 2007) as described in the GOHyperGAll module (Horan et al., 2008) implemented in the R programming language.”
• Identifying biologically relevant differences between metagenomic communities (2010, background): “Both one- and two-sided tests are supported, although for metagenomic studies two-sided results should generally be reported for the reasons given in Rivals et al. (2007).”
• Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “The concept here is that these methods perform better to capture the non-linear mapping from genotypes to phenotypes [4, 8–11].”
• Missing heritability and strategies for finding the underlying causes of complex disease (2010, background): “As such, solving part of the missing heritability problem will require the application of statistical and computational methods that detect patterns of epistasis across the genome implemented in a systems biology framework that accounts for the highly interconnected nature of biomolecular network”
• GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures (2012, background): “Computational geneticists are putting significant effort into developing algorithms for the detection of complex disease associations within clinical data [6-9].”
• LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data (2009, background): “It is applicable in experiments involving two or more sample types, and accepts significance statistics of the investigator’s choice as input.”