TAILIEUCHUNG - Global assessment of network inference algorithms based on available literature of gene/ protein interactions

We propose a framework that uses the available gene/protein interaction databases of the literature as a universal benchmark in order to globally assess the inference performances of gene network inference algorithms. | Turkish Journal of Biology Turk J Biol (2013) 37: 547-555 © TÜBİTAK doi: Research Article Global assessment of network inference algorithms based on available literature of gene/ protein interactions 1, 2,3 2 Gökmen ALTAY *, Nejla ALTAY , David NEAL Department of Biomedical Engineering, Bahçeşehir University, Beşiktaş, İstanbul, Turkey 2 Cancer Research UK Cambridge Research Institute, Department of Oncology, University of Cambridge, Cambridge, UK 3 Department of Mathematics, Bahçeşehir University, Beşiktaş, İstanbul, Turkey 1 Received: Accepted: Published Online: Printed: Abstract: We propose a framework that uses the available gene/protein interaction databases of the literature as a universal benchmark in order to globally assess the inference performances of gene network inference algorithms. We also developed an R software package for convenient use of the framework, which can also be used in general as a quick tool to search in the literature for available validations of interactions. We applied the proposed approach to 2 publicly available prostate cancer gene expression datasets and a large breast cancer gene expression dataset. The results revealed different aspects and superiority of algorithms that had not been compared previously in the available literature. Our approach allowed the assessing and comparing of the algorithms on a real dataset of a size of around 30,000 probes, which showed the strengths and weaknesses of the algorithms from different points of view rather than conventional approaches. We further show that our approach provides a unique advantage in assessing the performance of an inference method when applied to a new dataset and thus sheds light on the results of a de novo application, which would be obscure without our approach. Key words: Gene network inference, bioinformatics, gene/protein interaction databases 1. .

• Sinh học
• Gene network inference
• Protein interaction databases
• Gene interaction databases
• Gene network inference algorithms
• False positives
• Gene regulatory network inference
• Systems biology despite
• Supervised network inference
• Markov Logic network
• Inference algorithms
• BMC Bioinformatics
• Gene regulatory networks
• Network inference
• Gene expression data
• High throughput data
• Time series expression data
• Prior information fusion
• Inferring gene regulatory networks
• Gene regulatory network
• Design of experiments
• Signed directed graph
• Transitive reduction
• Gene network reconstruction
• Gene regulation
• Penalized regression
• Time course data
• Reverse engineering
• Discrete optimization
• Graph cuts
• Gene regulatory
• Partial least squares
• Single cell transcriptomics
• Multiscale modelling
• High parallel computing
• Approximate inference
• Gene regulatory network models
• RNA Seq time
• Horseshoe prior
• Random subspace method
• Biological interpretation
• Edge interactions
• Phylogenetic network
• Phylogenetic inference
• Large scale
• Incomplete lineage sorting
• Gene flow
• Bioconductor package
• Gene expression
• Gene regulation network reconstruction
• Synthetic genetic networks
• Genome Biology
• Cancer genomics
• Computational systems biology
• Network biology
• Network propagation
• Gene copy number mutations
• Systems biology
• Gradient matching
• Gene network
• Differential network
• Proximal gradient method
• TCGA database
• Gene modules
• Coordinately expressed genes
• High order dynamic Bayesian Network
• Hidden common cause
• Causality inference
• BMC Genetics
• Differentially expressed genes
• Epithelial membrane protein 2
• Retinal pigment epithelial cells
• Computational inference
• Cardiac differentiation
• Logistic regression
• Data integration
• Transcription regulation
• Time series
• Identifying regulons
• BMC Genomics
• Graphical user interface
• Multifactorial transcriptomic analysis
• Model based clustering
• Analysis workflow
• RNA seq
• Differential gene expression
• Psychrotrophic lactic acid bacteria
• Cold and heat shock
• Bayesian networks
• Dynamic networks
• Fusarium head blight
• BMC Cancer
• Software tools
• Computational biology
• Biological networks
• Network analysis
• Precision medicine