Function Prediction of Uncharacterized Proteins

Hawkins, Troy; Kihara, Daisuke

doi:10.1142/s0219720007002503

Cited by 99 publications

(70 citation statements)

References 158 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The function of a protein derives from its structure, but not always a high structural similarity corresponds to a high functional similarity and vice versa. 2,3 For example, the Human protein hIL-10 and the EpsteinÀBarr virus protein vIL-10 share a high structural similarity, but they have a clear distinct physiological pro¯le. 4 Divergence of function with sequence conservation is an exception rather than a rule, so in general sequence similarity remains as a reliable technique to determine the functional similarity of proteins.…”

Section: Why Functional Similarity?mentioning

confidence: 99%

The Next Generation of Similarity Measures That Fully Explore the Semantics in Biomedical Ontologies

Couto

Pinto

2013

J. Bioinform. Comput. Biol.

View full text Add to dashboard Cite

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current e®ort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this e®ort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although bene¯cial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of¯nding how this rich and powerful knowledge can be fully explored while keeping feasible computational costs. This manuscript aims at promoting and guiding the development of compelling tools that deliver what the biomedical community will require in a near future: a next-generation of biomedical similarity measures that e±ciently and fully explore the semantics present in biomedical ontologies.

show abstract

Section: Why Functional Similarity?mentioning

confidence: 99%

The Next Generation of Similarity Measures That Fully Explore the Semantics in Biomedical Ontologies

Couto

Pinto

2013

J. Bioinform. Comput. Biol.

View full text Add to dashboard Cite

show abstract

“…The elucidation of mechanisms underlying protein functionality is an important pursuit and remains a challenging task in computational biology [27,41]. Sequence similarity search methods like BLAST [3] are the primary tools for this problem.…”

Section: Protein Function Prediction Using Biological Networkmentioning

confidence: 99%

“…However, a non-negligible proportion of protein sequences do not have identifiable informative homologs in current databases. Therefore, a variety of new bioinformatics methods have been developed for inferring protein function using "guilt by association" of other functional properties to complement sequence similarity search [27].…”

Section: Protein Function Prediction Using Biological Networkmentioning

confidence: 99%

Using Biological Networks in Protein Function Prediction and Gene Expression Analysis

Wong

2011

Internet Mathematics

View full text Add to dashboard Cite

While sequence homology search has been the main work horse in protein function prediction, it is not applicable to a significant portion of novel proteins that do not have informative homologs in sequence databases. Similarly, while statistical tests and learning algorithms based purely on gene expression profiles have been popular for analyzing disease samples, critical issues remain in the understanding of diseases based on the differentially expressed genes suggested by these methods. In the past decade, a large number of databases providing information on various types of biological networks have become available. These databases make it possible to tackle these and other biological problems in novel ways. This paper presents a review on biological network databases and on approaches to protein function prediction and gene expression profile analysis that are based on biological networks.

show abstract

“…Automatic gene function prediction is an important and popular research topic in the bioinformatics community (Hawkins and Kihara 2007). Different types of approaches have been proposed to utilize sequence-based gene information (e.g., Jensen et al 2003), gene expression profiles (e.g., Eisen et al 1998) and comparative genomics methods (e.g., Marcotte et al 2000) for automatic gene function prediction.…”

Section: Related Workmentioning

confidence: 99%

Combining gene sequence similarity and textual information for gene function annotation in the literature

Fang

2008

Inf Retrieval

Self Cite

View full text Add to dashboard Cite

Annotation of the functions of genes and proteins is an essential step in genome analysis. Information extraction techniques have been proposed to obtain the function information of genes and proteins in the biomedical literature. However, the performance of most information extraction techniques of function annotation in the biomedical literature is not satisfactory due to the large variability in the expression of concepts in the biomedical literature. This paper proposes a framework to improve the gene function annotation in the literature by considering both the textual information in the literature and the functions of genes with sequences similar to a target gene. The new framework collects multiple types of evidence as: (i) textual information about gene functions by matching keywords of the gene functions; (ii) gene function information from the known functions of genes with sequences similar to a target gene; and (iii) the prior probabilities of gene functions to be associated with an arbitrary gene by mining the known gene functions from curated databases. A supervised learning method is utilized to obtain the weights for combining the three types of evidence to assign appropriate Gene Ontology terms for target genes. Empirical studies on two testbeds demonstrate that the combination of sequence similarity scores, function prior probabilities and textual information improves the accuracy of gene function annotation in the literature. The F-measure scores obtained with the proposed framework are substantially higher than the scores of the solutions in prior research.

show abstract

Function Prediction of Uncharacterized Proteins

Cited by 99 publications

References 158 publications

The Next Generation of Similarity Measures That Fully Explore the Semantics in Biomedical Ontologies

The Next Generation of Similarity Measures That Fully Explore the Semantics in Biomedical Ontologies

Using Biological Networks in Protein Function Prediction and Gene Expression Analysis

Combining gene sequence similarity and textual information for gene function annotation in the literature

Contact Info

Product

Resources

About