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Data mining opens the door to predictive neuroscience
Date:4/11/2012

position within the six, anatomically distinct layers of the cortex. They found that, based on the classification data alone, they could predict those previously measured ion channel patterns with 78 per cent accuracy. And when they added in a subset of data about the ion channels to the classification data, as input to their data-mining programme, they were able to boost that accuracy to 87 per cent for the more commonly occurring neuronal types.

"This shows that it is possible to mine rules from a subset of data and use them to complete the dataset informatically," says one of the study's authors, Felix Schrmann. "Using the methods we have developed, it may not be necessary to measure every single aspect of the behaviour you're interested in." Once the rules have been validated in similar but independently collected datasets, for example, they could be used to predict the entire complement of ion channels presented by a given neuron, based simply on data about that neuron's morphology, its electrical behaviour and a few key genes that it expresses.

Researchers could also use such rules to explore the roles of different genes in regulating transcription processes. And importantly, if rules exist for ion channels, they are also likely to exist for other aspects of brain organisation. For example, the researchers believe it will be possible to predict where synapses are likely to form in neuronal networks, based on information about the ratio of neuronal types in that network. Knowledge of such rules could therefore usher in a new era of predictive biology, and accelerate progress towards understanding and modelling the brain.


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Contact: Lionel Pousaz
lionel.pousaz@epfl.ch
41-795-597-161
Ecole Polytechnique Fdrale de Lausanne
Source:Eurekalert  

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