Pages that link to "Q30761952"

The following pages link to A new web-based data mining tool for the identification of candidate genes for human genetic disorders (Q30761952):

Displaying 38 items.

• Severely incapacitating mutations in patients with extreme short stature identify RNA-processing endoribonuclease RMRP as an essential cell growth regulator (Q24535876) (← links)
• New methods for finding disease-susceptibility genes: impact and potential (Q24792574) (← links)
• POCUS: mining genomic sequence annotation to predict disease genes (Q24793367) (← links)
• Integration of text- and data-mining using ontologies successfully selects disease gene candidates (Q24803121) (← links)
• Speeding disease gene discovery by sequence based candidate prioritization (Q24811442) (← links)
• GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases (Q24812838) (← links)
• G2D: a tool for mining genes associated with disease (Q24813279) (← links)
• WebGestalt: an integrated system for exploring gene sets in various biological contexts (Q24813284) (← links)
• A web tool for finding gene candidates associated with experimentally induced arthritis in the rat. (Q24816653) (← links)
• A human phenome-interactome network of protein complexes implicated in genetic disorders (Q29614448) (← links)
• Analysis of protein sequence and interaction data for candidate disease gene prediction (Q31064465) (← links)
• Novel analytical methods applied to type 1 diabetes genome-scan data (Q33199660) (← links)
• Transactional Database Transformation and Its Application in Prioritizing Human Disease Genes (Q33720610) (← links)
• A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data. (Q33787296) (← links)
• Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach (Q33826887) (← links)
• DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases. (Q33876948) (← links)
• Differential expression pattern-based prioritization of candidate genes through integrating disease-specific expression data (Q33880471) (← links)
• Identifying the gene signatures from gene-pathway bipartite network guarantees the robust model performance on predicting the cancer prognosis (Q34003648) (← links)
• Text mining in cancer gene and pathway prioritization (Q34434966) (← links)
• Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. (Q34535404) (← links)
• Uncover disease genes by maximizing information flow in the phenome-interactome network (Q35051828) (← links)
• Genome-wide identification of genes likely to be involved in human genetic disease (Q35130251) (← links)
• CANDID: a flexible method for prioritizing candidate genes for complex human traits. (Q35571604) (← links)
• From syndrome families to functional genomics (Q35813867) (← links)
• Candidate gene identification approach: progress and challenges (Q36094737) (← links)
• Bioinformatics methods for identifying candidate disease genes (Q36474983) (← links)
• Network-based global inference of human disease genes (Q36713985) (← links)
• Comparative genomic mapping of uncharacterized canine retinal ESTs to identify novel candidate genes for hereditary retinal disorders. (Q37194577) (← links)
• Identifying gene-disease associations using centrality on a literature mined gene-interaction network (Q37283339) (← links)
• Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies (Q37383600) (← links)
• Computational approaches to disease-gene prediction: rationale, classification and successes. (Q37973492) (← links)
• Computational tools for prioritizing candidate genes: boosting disease gene discovery (Q38023145) (← links)
• Identification of Parkinson's disease candidate genes using CAESAR and screening of MAPT and SNCAIP in South African Parkinson's disease patients (Q38503340) (← links)
• Walking the interactome for prioritization of candidate disease genes (Q39140568) (← links)
• Approaches for recognizing disease genes based on network (Q42021472) (← links)
• Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes (Q46494646) (← links)
• Combining the interactome and deleterious SNP predictions to improve disease gene identification. (Q51855746) (← links)
• Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method (Q58915086) (← links)