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DIAN: a novel algorithm for genome ontological classification.

ABSTRACT: Faced with the determination of many completely sequenced genomes, computational biology is now faced with the challenge of interpreting the significance of these data sets. A multiplicity of data-related problems impedes this goal: Biological annotations associated with raw data are often not normalized, and the data themselves are often poorly interrelated and their interpretation unclear. All of these problems make interpretation of genomic databases increasingly difficult. With the current explosion of sequences now available from the human genome as well as from model organisms, the importance of sorting this vast amount of conceptually unstructured source data into a limited universe of genes, proteins, functions, structures, and pathways has become a bottleneck for the field. To address this problem, we have developed a method of interrelating data sources by applying a novel method of associating biological objects to ontologies. We have developed an intelligent knowledge-based algorithm, to support biological knowledge mapping, and, in particular, to facilitate the interpretation of genomic data. In this respect, the method makes it possible to inventory genomes by collapsing multiple types of annotations and normalizing them to various ontologies. By relying on a conceptual view of the genome, researchers can now easily navigate the human genome in a biologically intuitive, scientifically accurate manner.

SUBMITTER: Pouliot Y

PROVIDER: S-EPMC311153 | biostudies-literature | 2001 Oct

REPOSITORIES: biostudies-literature

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DIAN: a novel algorithm for genome ontological classification.

Pouliot Y Y Gao J J Su Q J QJ Liu G G GG Ling X B XB

Genome research 20011001 10

Faced with the determination of many completely sequenced genomes, computational biology is now faced with the challenge of interpreting the significance of these data sets. A multiplicity of data-related problems impedes this goal: Biological annotations associated with raw data are often not normalized, and the data themselves are often poorly interrelated and their interpretation unclear. All of these problems make interpretation of genomic databases increasingly difficult. With the current e ...[more]

PMID: 11591654

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Project description:BackgroundA variety of key activities within life sciences research involves integrating and intelligently managing large amounts of biochemical information. Semantic technologies provide an intuitive way to organise and sift through these rapidly growing datasets via the design and maintenance of ontology-supported knowledge bases. To this end, OWL-a W3C standard declarative language- has been extensively used in the deployment of biochemical ontologies that can be conveniently organised using the classification facilities of OWL-based tools. One of the most established ontologies for the chemical domain is ChEBI, an open-access dictionary of molecular entities that supplies high quality annotation and taxonomical information for biologically relevant compounds. However, ChEBI is being manually expanded which hinders its potential to grow due to the limited availability of human resources.ResultsIn this work, we describe a prototype that performs automatic classification of chemical compounds. The software we present implements a sound and complete reasoning procedure of a formalism that extends datalog and builds upon an off-the-shelf deductive database system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. Furthermore, we describe a surface 'less-logician-like' syntax that allows application experts to create ontological descriptions of complex biochemical objects without prior knowledge of logic. In terms of performance, a noticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language suitable for the life sciences domain that exhibits a favourable balance between expressive power and practical feasibility.ConclusionsOur proposed methodology can form the basis of an ontology-mediated application to assist biocurators in the production of complete and error-free taxonomies. Moreover, such a tool could contribute to a more rapid development of the ChEBI ontology and to the efforts of the ChEBI team to make annotated chemical datasets available to the public. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm based on rules for which state-of-the-art and highly optimised reasoners are available; it could thus pave the way for the representation of a broader spectrum of life sciences and biomedical knowledge.

Project description:Background The classification for HD was developed by Goligher in 1980 and does not contemplate important aspects of this disease, which limits its use in guiding treatment. The aim of this study if to apply in clinical practice the new classification for hemorrhoids named BPRST (bleeding, prolapse, reduction, skin tags, thrombosis), to compare it with the original classification proposed by Goligher and to propose an algorithm for treatment. Materials and methods This is a prospective study conducted at the University of São Paulo's teaching hospital and Hospital 9 de Julho. Patients with HD treated from March 2011 to July 2013 were included. Patients were classified according to BPRST and Goligher classifications and treated according to personal experience and most updated guidelines. The association between both classifications and the treatment adopted was compared and an algorithm for treatment was developed. Results 229 patients were included in this study and 28 patients were lost due to follow-up. According to Goligher, 29, 61, 85 and 26 were classified as grades I, II, III and IV, respectively. According to the BPRST, 23 were classified as stage I, 95 as stage II and 83 as stage III. Six patients classified as Goligher I were reclassified as BPRST stage III and required conventional hemorrhoidectomy, either due to thrombosis (n = 4) or intolerable skin tags (n = 2). The BPRST classification was more closely associated with the type of treatment employed and had few outliers than Goligher (p < 0.001). Conclusion There are limitations to the use of Goligher's classification in clinical practice. The novel BPRST classification includes important aspects of HD that should be considered when deciding the best treatment option. Our algorithm for treatment contemplates the most commonly used techniques and can help to guide the treatment of this complex disease. Highlights • A new classification for hemorroidal disease• An algorithm for treatment of hemorrhoidal disease• A cohort study of hemorrhoidal disease and a novel classification

Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.

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DIAN: a novel algorithm for genome ontological classification.

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DIAN: a novel algorithm for genome ontological classification.

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