Inference on Missing Values in Genetic Networks Using High-Throughput Data

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Pietro Liò ◽  
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J. W. Yoon ◽  
Doosan Cho ◽  
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Xing-Ming Zhao ◽  
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Cell Cycle ◽  
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Lian Duan ◽  
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Vol 71 (2) ◽  
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Author(s):  
Boas Pucker ◽  
Hanna Marie Schilbert ◽  
Sina Franziska Schumacher

Combined awareness about the power and limitations of bioinformatics and molecular biology enables advanced research based on high-throughput data. Despite an increasing demand for scientists with a combined background in both fields, the education in dry lab and wet lab is often separated. This work describes an example of integrated education with focus on genomics and transcriptomics. Participants learn computational and molecular biology methods in the same practical course. Peer-review is applied as a teaching method to foster cooperative learning of students with heterogeneous backgrounds. Evaluation results indicate acceptance and appreciation of this approach.


Author(s):  
Boas Pucker ◽  
Hanna Marie Schilbert ◽  
Sina Franziska Schumacher

Combined awareness about the power and limitations of bioinformatics and molecular biology enables advanced research based on high-throughput data. Despite an increasing demand for scientists with a combined background in both fields, the education in dry lab and wet lab is often separated. This work describes an example of integrated education with focus on genomics and transcriptomics. Participants learn computational and molecular biology methods in the same practical course. Peer-review is applied as a teaching method to foster cooperative learning of students with heterogeneous backgrounds. Evaluation results indicate acceptance and appreciation of this approach.


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