This document discusses advanced bioinformatics methods for proteomics, specifically focusing on three parts: signaling networks, association networks, and text mining. It describes using phosphoproteomics to map signaling networks and identify unknown kinases and binding proteins. It also discusses using databases like STRING to map association networks through computational predictions, experimental data, and curated knowledge from pathways. Finally, it covers using natural language processing and text mining to extract information from the large amounts of biomedical literature in order to recognize entities and extract relations to help understand proteomics data.
85. Gene and protein names
Cue words for entity recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]
91. Acknowledgments
NetPhorest NetworKIN STRING Text-
Rune Linding
Martin Lee Miller
Rune Linding
Heiko Horn
Christian von Mering
Damian Szklarczyk
mining
Erwin Schoof Gerard Ostheimer Michael Kuhn Sune Frankild
Francesca Diella Martin Lee Miller Manuel Stark Evangelos Pafilis
Claus Jørgensen Francesca Diella Samuel Chaffron Janos Binder
Michele Tinti Karen Colwill Chris Creevey Heiko Horn
Lei Li Jing Jin Jean Muller Michael Kuhn
Marilyn Hsiung Pavel Metalnikov Tobias Doerks Nigel Brown
Sirlester A. Parker Vivian Nguyen Philippe Julien Reinhardt Schneider
Jennifer Bordeaux Adrian Pasculescu Alexander Roth Sean O’Donoghue
Thomas Sicheritz-Pontén Jin Gyoon Park Milan Simonovic
Marina Olhovsky Leona D. Samson Jan Korbel
Adrian Pasculescu Rob Russell Berend Snel
Jes Alexander Peer Bork Martijn Huynen
Stefan Knapp Michael Yaffe Peer Bork
Nikolaj Blom Tony Pawson
Peer Bork
Shawn Li
Gianni Cesareni
Tony Pawson
Benjamin E. Turk
Michael B. Yaffe
Søren Brunak