We also develop a workbench Topp Mi R based on this framework to infer significant micro RNAs and m RNA targets given a biological context.
Wu, Chao "Intelligent Data Mining on Large-scale Heterogeneous Datasets and its Application in Computational Biology." Electronic Thesis or Dissertation.
Present some papers and your idea to your prospective supervisor and he/she will make some suggestions. First, talk to your thesis advisor before committing to a project. Secondly, just analyzing a new dataset using standard techniques doesn't make for a good masters thesis.
Researchers generally have a lot of knowledge about the possibilities and might even be curious about some things themselves. Your project is expected to use some sort of novel approach.
Please suggest some topics or project that would make for a good masters thesis subject. Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise.
I think this has advantages because these papers outline details regarding data as well -- perhaps you can use the same.Machine learning is a branch of generic artificial intelligence, which covers a wide range of learning topics.A variety of supervised and unsupervised models of machine learning/data mining have been applied extensively in biomedical informatics studies for knowledge discovery.Please note that the list below is only a small sample of possible thesis topics and ideas.Please contact us to discuss further, to find new topics, or to suggest a topic of your own.With that said, I'd suggest that you start by reading up on existing decision tree techniques, learning why they work and what their flaws are, and try to find ways to overcome the flaws.Then, once you have your improvement, it should be relatively easy to find a dataset to apply it to.Second, we propose network-based approaches to predict drug repositioning candidates.These computational models utilize heterogeneous genomic and pharmacological information to generate potential drug repositioning candidates.We apply this specifically to the problem of micro RNA target ranking.We propose a framework that applies a series of data mining methods to prioritize entities in a heterogeneous network context.