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Machine Learning for Systems Biosciences
Published on Nov 16, 20123225 Views
The above title currently provides the best single label for the topics covered by my research group. Here I provide a brief summary of our current research and some of the research directions we inte
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Chapter list
Machine Learning for Systems Biosciences00:00
Primary Research Area: Machine Learning00:18
Application Area: Systems Biosciences00:52
The Research Agenda of My Group01:36
Equation Discovery: An Example02:08
Equation Discovery: Automated Modeling of Dynamic Systems02:51
Issues in Learning ODE Models from Data03:42
Domain Knowledge in Equation Discovery05:11
Process Models (PM)05:54
PM: Integration of the Two Aspects06:17
Inductive Process Modelling06:36
Major Developments06:46
Automated Modeling of Dynamic Systems in Ecology07:12
Automated Modeling of Lake EcoSystems07:22
Systems Biology: The Dynamics of Endocytosis07:28
Recent Advances and On-Going Work07:46
Data Mining: Predictive Modeling08:21
Predictive modeling: Structured input08:38
Predictive modeling: Structured output08:52
Multi-Target (Multi-Label) Prediction09:00
Multi-Target Prediction09:21
Predictive modeling: Structured output09:41
A predictive model for structured output10:05
Predicting short time series10:08
Predicting structured outputs10:34
Research topics10:36
Structured Output Prediction: Environmental Apps11:58
Genome-wide gene function prediction12:46
Identifying ‘Drug target’ Genes14:36
The Interplay of Methods and Applications15:37