Main results achieved
The main results achieved so far in ArSInformatiCa project include the papers presented on international scientific conferences both in Europe and in USA.
The first paper, which was presented in Europe, is the result of the work performed in work package WP1. Training in advanced methods of population genetics and bio-statistics as well as studying various models of human population evolution were the basis for development of the advanced versions of scientific software for calculating time to coalescence for branching processes. This software has been used for forward-in time simulation of evolution of human population modeled by slightly-critical branching process. In addition the work in work package WP1 resulted in formulating the Bayesian model of the genetic drift in branching processes, an evolutionary force potentially responsible for eliminating the hypothetical admixture of Neandertal mtDNA from Upper Paleolithic anatomically modern humans gene pool. This model has been used in conjunction with recent data on Neandertal Genome Project, supplementing the model with prior distributions, to estimate the most probable amount of Neandertal mtDNA admixture in a mtDNA gene pool of H. sapiens population at that time.
The second paper, presented at international conference in USA is the result of the work performed both in work package WP1, task T1.2 (development of Multi-Null Hypotheses MNH method) and WP2, task T2.1 as it refers to verification of MNH method as an expert knowledge generator in a search for natural selection in genes implicated in human familial cancers. The results achieved fully confirmed suitability of MNH method applied in that role. While it is computationally demanding (due to necessity to perform intensive computer simulations in order to obtain critical values of neutrality tests applied against modified null hypotheses), and therefore not applicable for wide search in many genes, it generates accurate knowledge, subject to use in machine learning methods for generalizing that knowledge and use it in fast machine-learning based inferring.
Finally, As the result of work package WP4, a scientific collaboration between Rice University in USA and three Polish research institutions: Silesian University of Technology, Center of Oncology, and University of Wroclaw has been established. It resulted in a common proposal for a grant on multidisciplinary research in evolution of cancer.