We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate statistical inference from such data is difficult due to the small number of observations, typically tens, relative to the large number of genes, typically thousands. Moreover, conventional methods from machine learning lead to decisions which are usually very difficult to interpret in simple or biologically meaningful terms. In contrast, the TSP classifier provides decision rules which i) involve very few genes and only relative expression values (e.g., comparing the mRNA counts within a single pair of genes); ii) are both accurate and transparent; and iii) provide specific hypotheses for follow-up studies. In particular, the TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures. Finally, the TSP classifier is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.
We propose adaptive testing as a general mechanism for extracting information about stimuli from spike trains. Each test or question corresponds to choosing a neuron and a time interval and checking for a given number of spikes. No assumptions are made about the distribution of spikes or any other aspect of neural encoding. The chosen questions are those which most reduce the uncertainty about the stimulus, as measured by entropy and estimated from stimulus-response data. Our experiments are based on accurate simulations of responses to pure tones in the auditory nerve and are meant to illustrate the ideas rather than investigate the auditory system. The results cohere nicely with well-understood encoding of amplitude and frequency in the auditory nerve, suggesting that adaptive testing might provide a powerful tool for investigating complex and poorly understood neural structures.
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