about
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and ImplicationsNetwork and data integration for biomarker signature discovery via network smoothed T-statistics.Discovering disease-disease associations by fusing systems-level molecular dataProtein fold recognition using geometric kernel data fusion.Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.Kernel methods for large-scale genomic data analysis.Gene Prioritization by Compressive Data Fusion and ChainingMethods for the integration of multi-omics data: mathematical aspectsMining breast cancer genes with a network based noise-tolerant approachIntegration of multiple data sources to prioritize candidate genes using discounted rating systemL2-norm multiple kernel learning and its application to biomedical data fusion.An integrated approach to inferring gene-disease associations in humans.An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.Robust rank aggregation for gene list integration and meta-analysisCancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.Improving biomarker list stability by integration of biological knowledge in the learning process.Multimodal classification of Alzheimer's disease and mild cognitive impairment.Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression ProfilesCandidate gene identification approach: progress and challengesGenetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network.Automated annotation of Drosophila gene expression patterns using a controlled vocabulary.A Meta-Path-Based Prediction Method for Human miRNA-Target Association.Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease.A guide to web tools to prioritize candidate genes.Recent approaches to the prioritization of candidate disease genes.Candidate gene prioritization.The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment DiagnosisPrediction of kinase-specific phosphorylation sites using conditional random fields.HyDRA: gene prioritization via hybrid distance-score rank aggregation.Scuba: scalable kernel-based gene prioritization.A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease.Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.
P2860
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P2860
description
2007 nî lūn-bûn
@nan
2007 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Kernel-based data fusion for gene prioritization.
@ast
Kernel-based data fusion for gene prioritization.
@en
type
label
Kernel-based data fusion for gene prioritization.
@ast
Kernel-based data fusion for gene prioritization.
@en
prefLabel
Kernel-based data fusion for gene prioritization.
@ast
Kernel-based data fusion for gene prioritization.
@en
P2093
P356
P1433
P1476
Kernel-based data fusion for gene prioritization.
@en
P2093
Liesbeth M M van Oeffelen
Tijl De Bie
Yves Moreau
P304
P356
10.1093/BIOINFORMATICS/BTM187
P407
P577
2007-07-01T00:00:00Z