Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data

Paccanaro, Alberto (PI)
Bogre, Laszlo (CoI)

Project: Research

Status	Finished
Effective start/end date	1/06/08 → 31/05/11

Funding

Biotechnology and Biological Sciences Research Council BBSRC: £421,635.00

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

SDG 3 Good Health and Well-being

LanDis: the disease landscape explorer
Caniza, H., Caceres, J. J., Torres, M. & Paccanaro, A., Apr 2024, In: European Journal of Human Genetics. 32, p. 461–465 5 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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Heterogeneous data integration methods for patient similarity networks
Gliozzo, J., Mesiti, M., Notaro, M., Petrini, A., Patak, A., Puertas-Gallardo, A., Paccanaro, A., Valentini, G. & Casiraghi, E., 13 Jun 2022, In: Briefings in Bioinformatics. 23, 4, bbac207.
Research output: Contribution to journal › Article › peer-review

Open Access
Machine learning and network medicine approaches for drug repositioning for COVID-19
de Siquiera Santos, S., Torres, M., Galeano Galeano, D., Sanchez, M. D. M., Cernuzzi, L. & Paccanaro, A., 9 Nov 2021, (E-pub ahead of print) In: Cell Patterns.
Research output: Contribution to journal › Article › peer-review

Open Access

Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data

Project Details

Funding

UN Sustainable Development Goals

Research output

LanDis: the disease landscape explorer

Heterogeneous data integration methods for patient similarity networks

Machine learning and network medicine approaches for drug repositioning for COVID-19