Large scale machine learning challenges for systems biology
by dr. Yvan Saeys - Machine Learning and Data Mining group, Bioinformatics and Systems Biology Division, VIB-UGent Department of Plant Systems Biology
Due to technological advances, the amount of biological data, and the pace at which it is generated has increased dramatically during the past decade. To extract new knowledge from these ever increasing data sets, automated techniques such as data mining and machine learning techniques have become standard practice.
In this talk, I will give an overview of large scale machine learning challenges in bioinformatics and systems biology, highlighting the importance of using scalable and robust techniques such as ensemble learning methods implemented on large computing grids.
I will present some of our state-of-the-art tools to solve problems such as biomarker discovery, large scale network inference, and biomedical text mining at PubMed scale.
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Large scale machine learning challenges for systems biology
1. Large scale machine learning challenges for systems biology Yvan Saeys Bioinformatics and Evolutionary Genomics (BEG) Department of Plant Systems Biology, VIB/UGent [email_address]
15. From text mining to integrated networks [Saeys, Y., Van Landeghem, S., Van de Peer, Y. (2010) Event based text mining for integrated network construction. Journal of Machine Learning Research, Workshop and Conference proceedings 8, 112-121.] Binding/unspecied Regulation Phosphorylation Transcription Positive Regulation Negative Regulation
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17. Example: apoptosis pathway [Björne J, Ginter F, Pyysalo S, Tsujii J, Salakoski T. Scaling up Biomedical Event Extraction to the Entire PubMed (2010) In Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, pp. 28-36.
18. Case study 3: Large scale network inference Dream 5 Network Inference challenge
19. Problem setting Data V â n Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, Yvan Saeys, and Pierre Geurts (2010) Regulatory network inference with GENIE3: application to the DREAM5 challenge. Recomb Regulatory Genomics workshop. 805 4511 334 E. Coli 536 5950 333 S. Cerevisiae 160 2810 99 S. Aureus 805 1643 195 In silico # Chips # Genes # T ransc Factors Network
21. Results: gold standard evaluation In silico E. Coli S. Cerevisiae 5.81 GGM 22.711 Team 548 7.15 Lin. Regr. 3.22 ARACNE 23.93 CLR 28.75 Team 862 31.1 Team 776 34.02 Team 543 40.28 Genie3-RF Overall score
![Large scale machine learning challenges for systems biology Yvan Saeys Bioinformatics and Evolutionary Genomics (BEG) Department of Plant Systems Biology, VIB/UGent [email_address]](https://image.slidesharecdn.com/06yvan-110929012334-phpapp01/85/Large-scale-machine-learning-challenges-for-systems-biology-1-320.jpg)
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