Name
Abstract | ||
|---|---|---|
We present a methodology for biological data integration, aiming at building and analysing large functional networks which model complex genotype-phenotype associations. A functional network is a graph where nodes represent cellular components (e.g., genes, proteins, mRNA, etc.) and edges represent associations among such molecules. Different types of components may cohesist in the same network, and associations may be related to physical/biochemical interactions or functional/phenotipic relationships. Due to both the large amount of involved information and the computational complexity typical of the problems in this domain, the proposed framework is based on big data technologies (Spark and NoSQL databases). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621128 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Integrative approaches, biological networks, big data technologies | Graph,Biological data,Spark (mathematics),Computer science,Biological network,Functional networks,Theoretical computer science,NoSQL,Artificial intelligence,Big data,Machine learning,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia Giallombardo | 1 | 0 | 1.01 |
| Salvatore Morfea | 2 | 0 | 0.34 |
| Simona E. Rombo | 3 | 192 | 22.21 |