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IMPORTANT POINTS & DISCOVERIES
- Development of the first approach for pathway analysis that considers factors such as: the topology of the pathways, position of each individual gene on each pathway, and type of interactions between genes. This approach was implemented first as a web tool (Pathway-Express) part of the Onto-Tools suite maintained by Dr. Draghici's laboratory. Later, a modified version of this approach, called SPIA (Signaling Pathway Impact Analysis), was published and implemented as a Bioconductor package by Dr. Tarca, who also maintains the package.
- Development of the first gene set analysis method that downplays the importance of genes that appear in multiple gene sets and hence are not gene set specific. The method called PADOG (Pathway Analysis with Down-weighting of Overlapping Genes) is available as a Bioconductor package and includes a benchmark for gene set and pathway analysis methods.
- The PRB team (Adi L. Tarca and R. Romero) proposed a machine learning approach that received the best overall entrant award out of 54 teams in the IMPROVER Diagnostic Signature Challenge. This approach uses gene expression data to predict clinical outcomes. See https://www.sbvimprover.com/challenge-1. A Bioconductor package called maPredictDSC was created to implement this approach, as well as other functionalities useful for predictive model development.