The deregulation of non-coding RNAs (ncRNAs) has a functional role in cancer and other human disorders [1, 2]. Reconstructing and visualizing networks of ncRNAs interactions with diseases and candidate targeting genes is important to understand their regulatory mechanism in complex cellular systems. Within ncRNA-DB [3], we have recently imported and integrated associations among non-coding RNAs, protein coding genes, and associated diseases from ten on-line databases. Up to date it contains about 300 thousands associations. To improve the usability of such a complex integrated system, it has to be equipped with a methodology leading users to weight the connections linking the ncRNAs to genes and to diseases. We elaborate a scoring methodology based on literature mining, network analysis, and alignment-free sequence algorithms, to rank the ncRNA-disease and ncRNA-gene associations reported in ncRNA-DB. The Lit-Score takes into account the frequencies of co-occurrence in PubMed and in ncRNA-DB of the pairs ncRNA-gene, ncRNA-disease, and gene-disease.
A scoring methodology for an integrated network of non-coding RNAs and genetic diseases / Bonnici, V; Franco, G; Bombieri, N; Pulvirenti, A; Giugno, R. - (2015), pp. 1-2. (Intervento presentato al convegno BITS (Bioinformatics Italian Society) annual meeting Milan, Italy tenutosi a Milan nel 3-5 June 2015).
A scoring methodology for an integrated network of non-coding RNAs and genetic diseases
Bonnici V;
2015-01-01
Abstract
The deregulation of non-coding RNAs (ncRNAs) has a functional role in cancer and other human disorders [1, 2]. Reconstructing and visualizing networks of ncRNAs interactions with diseases and candidate targeting genes is important to understand their regulatory mechanism in complex cellular systems. Within ncRNA-DB [3], we have recently imported and integrated associations among non-coding RNAs, protein coding genes, and associated diseases from ten on-line databases. Up to date it contains about 300 thousands associations. To improve the usability of such a complex integrated system, it has to be equipped with a methodology leading users to weight the connections linking the ncRNAs to genes and to diseases. We elaborate a scoring methodology based on literature mining, network analysis, and alignment-free sequence algorithms, to rank the ncRNA-disease and ncRNA-gene associations reported in ncRNA-DB. The Lit-Score takes into account the frequencies of co-occurrence in PubMed and in ncRNA-DB of the pairs ncRNA-gene, ncRNA-disease, and gene-disease.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
