Anni Heikkilä, Eeva Sliz, Laura Huilaja, Kadri Reis, Priit Palta, Abdelrahman G Elnahas, Anu Reigo, Tõnu Esko, Triin Laisk, Maris Teder-Laving, Kaisa Tasanen, Johannes Kettunen
GENETIC STUDY OF PSORIASIS HIGHLIGHTS ITS CLOSE LINK WITH SOCIO-ECONOMIC STATUS AND AFFECTIVE SYMPTOMS
The Journal of investigative dermatology
joint analysis two datasets formula see text formula see text describe phenomena eg cellular state measure disjoint sets variables eg mrna vs protein levels currently challenging traditional methods typically analyze single interaction patterns variance covariance however problemtailored external knowledge may contain multiple different information interaction measured variables introduce miasa holistic framework joint analysis multiple different variables consists assembling multiple different information similarity vs association expressed terms interactionscores distances subsequent clusteringclassification addition framework includes novel qualitative euclidean embedding method qeetransition enables using euclideandistancevectorbased clusteringclassification methods datasets noneuclideanbased interaction structure alternative conventional optimizationbased multidimensional scaling methods prone uncertainties qeetransition generates new vector representation element dataset union formula see text common euclidean space strictly preserving original ordering assembled interactiondistances demonstrate work applied framework three types simulated datasets samples families distributions samples correlated random variables timecourses statistical moments three different types stochastic twogene interaction models compared different clustering methods vs without qeetransition examples found qeetransition followed ward clustering superior performance compared nonagglomerative clustering methods varied performance ultrametricbased agglomerative methods also tested qeetransition followed supervised unsupervised machine learning methods found promising results however work needed optimal parametrization methods future perspective framework points importance developments validation distancedistribution models aiming capture multiplecomplex interactions different variables