Závěrečná zpráva o řešení grantu Interní grantové agentury MZ ČR
1 svazek : ilustrace, tabulky ; 30 cm
This project significantly extends the current public XGENE.ORG web tool in order to facilitate integrated knowledge discovery from raw mRNA, miRNA and methylation data with concurrent utilization of the structured genomic background knowledge. There are two main project outputs: the tool (and the methodology behind it) itself and the particular results reached in cooperation with clinical and biological departments working in the fields of myelodysplastic syndrome and germ cell tumors. The solution is based on relational learning algorithms, stochastic optimization, statistics and development of web applications. The tool outputs namely 1) biologically understandable patterns having a form of sets or annotated networks of specific related elements such as genes, proteins, miRNA sequences or methylation islands interconnected with particular subsets of biological samples under study and 2) predictive models classifying samples characterized by measurable molecular markers with unknown phenotypes.
Projekt významně funkčně rozšiřuje stávající podobu veřejného XGENE.ORG web nástroje s cílem umožnit kombinaci a souhrnné vyhodnocení uživatelských mRNA, miRNA a metylačních dat se současným využitím všech vhodných zdrojů strukturované genomické znalosti. Kromě nástroje budou výstupem i výsledky dosažené ve spolupráci s konkrétními pracovišti zabývajícími se myelodysplastickým syndromem a tumory zárodečných buněk. Jádrem řešení budou pokročilé algoritmy relačního strojového učení a stochastické optimalizace, statistické metody a moderní metody pro tvorbu webových aplikací. Konkrétními výstupy nástroje budou 1) biologicky snadno popsatelné vzory ve tvaru množiny nebo anotované sítě specifických příbuzných elementů typu gen, bílkovina, miRNA sekvence nebo metylační ostrůvek spojených s konkrétní charakteristickou množinou biologických vzorků a 2) prediktivní modely určující neznámý fenotyp vzorku se známými hodnotami měřitelných molekulárních markerů.
- MeSH
- data mining MeSH
- databáze jako téma MeSH
- exprese genu MeSH
- genetická transkripce MeSH
- germinální a embryonální nádory MeSH
- informační systémy MeSH
- messenger RNA MeSH
- metylace MeSH
- mikro RNA MeSH
- myelodysplastické syndromy MeSH
- nádorové biomarkery MeSH
- počítačové systémy MeSH
- ukládání a vyhledávání informací MeSH
- využití lékařské informatiky MeSH
- Konspekt
- Biochemie. Molekulární biologie. Biofyzika
- NLK Obory
- genetika, lékařská genetika
- biologie
- lékařská informatika
- NLK Publikační typ
- závěrečné zprávy o řešení grantu IGA MZ ČR
BACKGROUND: Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. METHODS: We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. RESULTS: The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. CONCLUSION: Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.
BACKGROUND: Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set-level classifiers have yielded rather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set-level framework within a large collection of machine learning experiments. RESULTS: Genuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene-set methods GSEA and SAM-GS as well as two generic feature selection methods. To aggregate expressions of genes into a feature value, the singular value decomposition (SVD) method as well as the SetSig technique improve on simple arithmetic averaging. Set-level classifiers learned with 10 features constituted by the Global test slightly outperform baseline gene-level classifiers learned with all original data features although they are slightly less accurate than gene-level classifiers learned with a prior feature-selection step. CONCLUSION: Set-level classifiers do not boost predictive accuracy, however, they do achieve competitive accuracy if learned with the right combination of ingredients. AVAILABILITY: Open-source, publicly available software was used for classifier learning and testing. The gene expression datasets and the gene set database used are also publicly available. The full tabulation of experimental results is available at http://ida.felk.cvut.cz/CESLT.
