For the biological dataset, detailed analysis of the results suggests that DPC uncovers more biologically relevant regulatory relationships than the competing method SVAR.
Projects may be driven by a specific biological dataset (e.g. human genome variation, cancer genomes, etc.) or area of computer science (e.g. algorithms, machine learning, databases, visualization, etc.).
Projects may be driven by a specific biological dataset (e.g. human genome variation, cancer genomes, single-cell gene expression, etc). or area of computer science (e.g. algorithms, machine learning, databases, visualization, etc). Students may work individually or in small teams.
This supervised classification algorithm identifies linear combinations of the measured signals that maximally separate pre-defined groups within a biological dataset (in this case, infected versus uninfected cells) and then projects these onto a new set of "super" variables called principal components.
The performance of the methodology is studies via simulations and using a biological dataset of animal communication signals comprising 43 groups of electric signals recorded from tropical South American electric knife fishes.
The aim of this work is to analyze and explain this issue through a real case study involving transcriptomic and metabolomic data, since it might have an important impact when interpreting clustering results over a biological dataset.
The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI).
For the biological dataset, DPC appears to give more biological meaningful results than SVAR.
These fish collections have recently provided the most comprehensive and useful biological dataset for bioregionalization of Australian waters.
We also applied the three methods to a real biological dataset [9] to identify genes in cold stress regulatory pathways.
The third set is a known stock-market dataset, and forth is a biological dataset of ion-channel proteins.
