ABOUT US

We provide a system for extracting and analyzing the most informative elements from biological data. In sections below we present results and advantages obtained using GeneIntelligence software.

Find out more about how does it work

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SERVICESQuantify markers impact on the phenotype

Combining artificial intelligence (AI) algorithms with statistics enables to find a functional subset of statistically significant markers and quantify their p-value and strength of the effect.

SERVICESQuantify, visualize and compare markers

The core of GeneIntelligence technology is machine learning algorithms (MLA).
Our unique algorithm quantifies the correlation between genes and phenotypes.
Interactive charts visualize important factors such as level of methylation, expression or genotype frequency between phenotypes. Recent tests show that the performance of our tool is far more superior to standard techniques.

SERVICESVisualize and compare global differences between clusters

Results of the clustering algorithms are much easier to interpret if their work on a selected - less noisy (denoised) subset of data. A cluster-map calculated according e.g. a percentage methylation level (color scale) enables to group patients into ill (blue bar) and healthy (red bar).

SERVICESDISCOVER BOUNDARIES BETWEEN PHENOTYPES

The reduction of complexity composed with 2-dimensional projection methods allow to find out boundaries between groups of different phenotypes. In the example below, boundaries between groups strongly overlap in a noisy dataset [left]. The same dataset processed with the GeneIntelligence algorithm agglomerate data into separable groups [right].

2-D projection of full dataset

2-D projection of selected markers

SERVICESThe analytical procedure based on a decision tree algorithm

Decision trees - DE enable to conclude about the combinations of genes determining phenotype. DE facilitate understanding of complex dependencies between data. Furthermore, they allow telling apart examined groups represented as leaves.

ADVENTAGESReduction of size and complexity of initial dataset improves the quality of future analyses such as genetic mapping

Associated-with-trait markers, indicated by our application may be targeting into specific mapping region to increase its density. Genetic map [A] present chromosome constructed using a full set of markers, for comparison map [B] shows a dense region constructed on the basis of selected markers.