Predicting HIV Dependency Factors Using Network-Based Approaches
Author Information
Author(s): Murali T. M., Dyer Matthew D., Badger David, Tyler Brett M., Katze Michael G.
Primary Institution: Virginia Polytechnic Institute and State University
Hypothesis
The proximity of experimentally-detected HIV dependency factors within the human protein-protein interaction network can be exploited by machine-learning algorithms to predict novel HIV dependency factors.
Conclusion
The study successfully predicts new HIV dependency factors that may serve as prognostic markers for AIDS development.
Supporting Evidence
- The SinkSource algorithm achieved a precision of 81% at 20% recall.
- Predicted HIV dependency factors showed significant overlaps with known HIV interactors.
- Many predicted HDF genes displayed different expression patterns in response to SIV infection.
Takeaway
The researchers found new proteins that HIV needs to survive, which could help in developing better treatments for the disease.
Methodology
The study combined data from three genome-wide RNAi experiments with a human protein interaction network to predict new HIV dependency factors using various algorithms.
Potential Biases
The choice of negative examples may introduce bias, potentially overlooking some proteins that interact with essential proteins.
Limitations
The predictions may miss some essential proteins due to the reliance on non-essential human proteins as negative examples.
Participant Demographics
The study analyzed data from human proteins and their interactions with HIV.
Statistical Information
P-Value
2.1×10−7
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website