Aktualności Instytutu Podstaw Informatyki PAN

Streszczenie (autorskie):

The current huge successes of neural networks and deep learning sidelined the important aspect of transparency of the predictions made by these classifiers. While predictive accuracy is important in many applications, in the case of knowledge discovery in life sciences the main interest is in understanding how the predictions are made and thus some loss of accuracy may be accepted. In this seminar, I shall present our methodology based on the combination of Monte Carlo Feature Selection (rmcfs) and rough set R.ROSETTA developed in my laboratory that supports making discoveries in life sciences, but is general enough to contribute to predictive maintenance. Examples will include transcriptomic studies of Systemic Lupus Erythematosus, a complex immune system disease, progression of Acute Myeloid Leukemia and establishment of protection against SIV/HIV in Rhesus macaques upon vaccination. And for the predictive maintenance case, I shall use the model of water piping system in the Stockholm area developed in our laboratory.