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Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowl Inf Syst 2018. [DOI: 10.1007/s10115-018-1244-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Abstract
The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.
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Affiliation(s)
- Pascale Gaudet
- CALIPHO group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel-Servet, 1211, Geneva 4, Switzerland. .,Department of Human Protein Sciences, Faculty of Medicine, University of Geneva, 1211, Geneva, Switzerland.
| | - Christophe Dessimoz
- Department of Genetics, Evolution & Environment, University College London, Gower St, London, WC1E 6BT, UK.,Swiss Institute of Bioinformatics, Biophore Building, 1015, Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Street Biophore, 1015, Lausanne, Switzerland.,Center of Integrative Genomics, University of Lausanne, Biophore, 1015, Lausanne, Switzerland.,Department of Computer Science, University College London, Gower St, WC1E 6BT, London, UK
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Jiang Y, Clark WT, Friedberg I, Radivojac P. The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective. ACTA ACUST UNITED AC 2015; 30:i609-16. [PMID: 25161254 PMCID: PMC4147924 DOI: 10.1093/bioinformatics/btu472] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Motivation: The automated functional annotation of biological macromolecules is a problem of computational assignment of biological concepts or ontological terms to genes and gene products. A number of methods have been developed to computationally annotate genes using standardized nomenclature such as Gene Ontology (GO). However, questions remain about the possibility for development of accurate methods that can integrate disparate molecular data as well as about an unbiased evaluation of these methods. One important concern is that experimental annotations of proteins are incomplete. This raises questions as to whether and to what degree currently available data can be reliably used to train computational models and estimate their performance accuracy. Results: We study the effect of incomplete experimental annotations on the reliability of performance evaluation in protein function prediction. Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods. We then analyze real biological data by simulating the prediction, evaluation and subsequent re-evaluation (after additional experimental annotations become available) of GO term predictions. Our results agree with previous observations that incomplete and accumulating experimental annotations have the potential to significantly impact accuracy assessments. We find that their influence reflects a complex interplay between the prediction algorithm, performance metric and underlying ontology. However, using the available experimental data and under realistic assumptions, our results also suggest that current large-scale evaluations are meaningful and almost surprisingly reliable. Contact:predrag@indiana.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuxiang Jiang
- Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA
| | - Wyatt T Clark
- Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA
| | - Iddo Friedberg
- Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA
| | - Predrag Radivojac
- Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA
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