Predicting protein annotations using the expected network interaction between related diseases or cellular processes

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Date/heure
Date(s) - 29/04/2019
11 h 00 min - 12 h 00 min

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Genes or proteins involved in the same cellular process or disease tend to cluster together within biological networks. This fact is used by numerous algorithms to predict new protein functions or disease associations.

We hypothesize that knowing that two (or more) cellular processes or diseases are functionally related may improve our ability to predict new proteins associated with both processes or diseases. I will present two methods that are being developed in my research group based in the previous hypothesis.

The first is a specific version of betweenness centrality, called S2B, that predicts the simultaneous association of a protein with two related diseases. For each candidate protein, this method measures the frequency with which that protein is part of shortest paths linking proteins associated with one disease to proteins associated with the other disease.

The second method aims to expand the list of proteins associated with a seed cellular process. It first identifies other cellular processes that specifically interact with seed proteins of the queried cellular process. Seed direct neighbors are then compared with the seeds by their ability to bridge the query process with the specific neighbor processes. This comparison allows the classification of seed direct neighbors as being involved (or not) in the seed process.

http://ciencias.ulisboa.pt/pt/perfil/frpinto

Olivier CHABROL
Posts created 14

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