Thesis Data Mining

Thesis Data Mining-58
We validate the approach using known indications before applying to predict new indications for existing drugs.Third, we study several statistical and computational strategies to generate overall significance of relationships between different biological entities.

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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.

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 algorithm to predict combinatorial regulation of transcription factors.

We also extend the algorithm to generate 3-clusters in order to capture associations between different classes of entities.For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities.For more information about writing a master's theses in our group, please see here.In the Web Science Group, we are particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured data (on the Web). Felix Naumann Information Systems E-Mail: felix.naumann(at)Assistant: Diana Stephan Office: Campus II, House F, F-2.01 Tel.: 49 (0)331 5509-280 Fax: 49 (0)331 5509-287 E-Mail: office-naumann(at)To visit us, please see these directions.If you are too, maybe some of the following topics spark your interest. I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application.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. We strive to make available most of our data sets and source code.The information systems group is always looking for good master students to write master's theses.

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