Research, development, and application of technologiesIntelligent data analysis and modelling

Implementation of advanced algorithms and software development

ADINIS is actively exploring application of advanced machine learning algorithms in modeling, analysis and prediction of large scale multivariate data emerging in experimental biology, chemistry, medicine and other scientific disciplines. We strive to provide our customers with solutions of highest quality that require expert knowledge of computational domains (machine learning, statistical modeling, numerical analysis, and software engineering) as well as good understanding of modeled domain (proteomics, genomics, cytology,…).

Our researchers are in close contact with customers from an early stage of each project, often taking leading roles in analyzing customer’s needs and suggesting and designing appropriate solutions. Typically multiple computational approaches are explored in early prototypes and only the approach best fitting the customer’s needs is then implemented to a final software product. The table below specifies some of the techniques that we have used in our projects or have strong expertise in:

Machine Learning
Logistic regression
Neural Networks
PCA, MDS, Factor Analysis
Decision Trees, Random Forests
Maximum likelihood, graphical models, EM, probabilistic inference, MCMC
Cluster analysis, Kmeans, GMM
Hypothesis testing
General Linear Model
Outlier detection
Bioinformatics :
Dynamic programming, Needleman-Wunsch, Smith-Waterman
Quntitative Trait Locus