A Bayesian kernel for the prediction of neuron properties from binary gene profiles

Predicting cellular properties from molecular or genetic data is a challenge for bioinformatics and machine learning. In brain slices of neuronal tissue, it has become possible to both measure electro-physiological properties of a given neuron and to extract a sample of its cytoplasm so that expressed genes can be amplified. Thus, the presence or absence of genes related to ion channels in the neuronal cell membrane can be correlated with neuronal behavior encoded as a set of electro-physiological parameters. A typical gene amplification process is asymmetric in the sense that false positives are very rare, whereas false negatives (genes expressed but not amplified) are rather common. An analysis of a probabilistic model of that process yields a similarity measure between two strings of amplified genes that takes the asymmetry of the amplification process into account. This similarity measure can be put under the form of a conformal-transformed kernel. We provide experiments with support-vector machines on artificial and neuronal data.