They took a dataset that Prof Markram and others had collected a few years ago, in which they recorded the expression of 26 genes encoding ion channels in different neuronal types from the rat brain. They also had data classifying those types according to a neuron’s morphology, its electrophysiological properties and its 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 Schürmann. “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.

Cross-reference the #connectome debate from this lecture: https://plus.google.com/u/0/117828903900236363024/posts/Ky5piPLjhYd

Neuroscience News originally shared this post:

Data Mining Opens the Door to Predictive Neuroscience

Researchers at the EPFL have discovered rules that relate the genes that a neuron switches on and off, to the shape of that neuron, its electrical properties an