Type and enter
Integrative Modeling for Systems Biology: Understanding from multiple data set and knowledge.
Understanding the use of cross-platform data integration combined with knowledge to learn the architecture of biological systems. Presented at EBI-Cambridge (UK) June, 2013 5.
rom experiments to pathway and back: Reverse engineering approach.
A talk on how to use experimental data to engineer biological networks and use the networks to engineer new experiments for deeper insights at systems level. Presented at Swiss Institute of Bioinformatics, Laussane (Switzerland) 2013 4.
Constructing informative prior from multiple knowledge sources to improve network inference.
An extension of the talk 3 towards Nested Effects Models (NEM) The prior knowledge extended to learn from perturbation effects. The talk was presented at Rocky Mountain bioinformatics Conference Aspen, CO (USA) 3.
From experiments to pathways
The talk presented during a symposium at B-IT Bonn. The talk is about using perturbation High Throughput (HT)-data and compute pathways out of it. The created model and knowledge can then be further used to design new experiments and propose hypotheses 2.
Boosting Statistical Network Inference by Incorporating Prior Knowledge from Multiple Sources.
The talk presented during Machine Learning for Systems Biology symposium at ECCB 2012 Basel (Switzerland). The talk is about methods to integrate quantitative prior from existing biological learning to learn networks from HT data via Bayesian Networks. 1.
An introduction to R-programming.
A short introduction regarding basics of programming in R. Discusses the basic operations loops, functions structure, data and text handling with some ploting stuff.
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© Paurush Praveen (2017)