Journal ArticleDOI
True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
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TLDR
Cross-validated results with the model organism S. Crevisiae, using seven different sources of biomolecular data, and a theoretical analysis of the the TPR algorithm show the effectiveness and the drawbacks of the proposed approach.Abstract:
Gene function prediction is a complex computational problem, characterized by several items: the number of functional classes is large, and a gene may belong to multiple classes; functional classes are structured according to a hierarchy; classes are usually unbalanced, with more negative than positive examples; class labels can be uncertain and the annotations largely incomplete; to improve the predictions, multiple sources of data need to be properly integrated. In this contribution, we focus on the first three items, and, in particular, on the development of a new method for the hierarchical genome-wide and ontology-wide gene function prediction. The proposed algorithm is inspired by the “true path rule” (TPR) that governs both the Gene Ontology and FunCat taxonomies. According to this rule, the proposed TPR ensemble method is characterized by a two-way asymmetric flow of information that traverses the graph-structured ensemble: positive predictions for a node influence in a recursive way its ancestors, while negative predictions influence its offsprings. Cross-validated results with the model organism S. Crevisiae, using seven different sources of biomolecular data, and a theoretical analysis of the the TPR algorithm show the effectiveness and the drawbacks of the proposed approach.read more
Citations
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Journal ArticleDOI
Predicting protein functions using incomplete hierarchical labels
TL;DR: The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels and is shown to outperform other related techniques in replenishing the missing labels and in predicting the functions of completely unlabeled proteins on publicly available PPI datasets annotated with MIPS Functional Catalogue and Gene Ontology labels.
Proceedings Article
Hierarchical Multi-Label Classification Networks
TL;DR: A novel neural network architectures for HMC called HMCN is proposed, capable of simultaneously optimizing local and global loss functions for discovering local hierarchical class-relationships and global information from the entire class hierarchy while penalizing hierarchical violations.
Journal ArticleDOI
Hierarchical multi-label classification using local neural networks
TL;DR: A new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy, and obtains competitive results to a robust global method regarding both precision and recall evaluation measures.
Book ChapterDOI
Ensemble methods : a review
Matteo Re,Giorgio Valentini +1 more
TL;DR: Ensemble methods: a review 3 Matteo Re and Giorgio Valentini 1.1 Ensemble methods : a review
Journal ArticleDOI
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference
TL;DR: The experiments show that key factors for the success of hierarchical ensemble methods are the integration and synergy among multilabel hierarchical, data fusion, and cost-sensitive approaches, as well as the strategy of selecting negative examples.
References
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Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI
The Pfam protein families database
Marco Punta,Penny Coggill,Ruth Y. Eberhardt,Jaina Mistry,John Tate,Chris Boursnell,Ningze Pang,Kristoffer Forslund,Goran Ceric,Jody Clements,Andreas Heger,Liisa Holm,Erik L. L. Sonnhammer,Sean R. Eddy,Alex Bateman,Robert D. Finn +15 more
TL;DR: The definition and use of family-specific, manually curated gathering thresholds are explained and some of the features of domains of unknown function (also known as DUFs) are discussed, which constitute a rapidly growing class of families within Pfam.
Journal ArticleDOI
The Elements of Statistical Learning
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
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