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Advanced bioinformatics methods for proteomics
The document discusses several advanced bioinformatics methods and computational tools for proteomics research, including NetPhorest, STRING, Reflect, and NetworKIN. It describes how these tools use machine learning techniques and large protein-protein interaction datasets to build models for predicting kinase-substrate relationships and mapping sequence motifs, helping to organize big proteomics data and generate testable hypotheses to guide experimental validation work.
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Advanced bioinformatics methods for proteomics
- 1. Advanced bioinformatics methods forproteomics Lars Juhl Jensen
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- 76. thank you! NetPhorest – RuneLinding – Martin Lee Miller – Francesca Diella – Claus Jørgensen – Michele Tinti – Lei Li – Marilyn Hsiung – Sirlester A. Parker – Jennifer Bordeaux – Thomas Sicheritz-Pontén – Marina Olhovsky – Adrian Pasculescu – Jes Alexander – Stefan Knapp – Nikolaj Blom – Peer Bork – Shawn Li – Gianni Cesareni – Tony Pawson – Benjamin E. Turk – Michael B. Yaffe – Søren Brunak STRING – Christian von Mering – Damian Szklarczyk – Michael Kuhn – Manuel Stark – Samuel Chaffron – Chris Creevey – Jean Muller – Tobias Doerks – Philippe Julien – Alexander Roth – Milan Simonovic – Jan Korbel – Berend Snel – Martijn Huynen – Peer Bork Reflect – Sune Frankild – Heiko Horn – Evangelos Pafilis – Michael Kuhn – Nigel Brown – Reinhardt Schneider – Sean O’Donoghue NetworKIN – Rune Linding – Heiko Horn – Gerard Ostheimer – Martin Lee Miller – Francesca Diella – Karen Colwill – Jing Jin – Pavel Metalnikov – Vivian Nguyen – Adrian Pasculescu – Jin Gyoon Park – Leona D. Samson – Rob Russell – Peer Bork – Michael Yaffe – Tony Pawson
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