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Microarray analysis using machine learning

By Pablo Martin, Artelnics.

Data mining is being widely used in the field of health. Studying genetic factors, environmental influences and physiological data allows practitioners to prevent, diagnose and treat diseases more effectively in order to improve people's welfare. The main challenge for predictive analytics is to use artificial intelligence to analyze clinical data in order to take up new models of care and new technologies promoting health and wellbeing.

Functional genomics involves the analysis of large datasets of information derived from various biological experiments. One such type of large-scale experiment involves monitoring the expression levels of thousands of genes simultaneously under a particular condition, called gene expression analysis. Microarray technology makes this possible and the quantity of data generated from each experiment is enormous. Data mining can give a feeling for what the data actually represents, derive meaningful results from such experiments.


Analyze the changes in the genes to find patterns and determine if the are in the normal or disease state.
Determine the conditions that make a gene change from normal to disease state.
Develop a model that can recognize the changes in the genes and predict the state of them.
Use the predictive model for preventive medicine and early diagnosis.

It is important that all of the information about a microarray experiment is recorded systematically, so that meaningful data sets can be generated.In addition, it is important to apply the correct analysis techniques to obtain the best possible results.

Lab picture

Neural Designer is a software that implements neural networks, a machine learning technique capable of analyzing large amount of data and find complex relation between them.

In addition, Neural Designer user interface will provide you with different methods to analyze relations between the state of the genes and the environmental conditions, model selection algorithms for the best design of the neural network and a set of testing methods to check the correct functioning of the predictive model.

The next image shows a summary of the process that would be followed to obtain a predictive model for this case. Firstly, the data about DNA are arranged in a database. This database is analyzed by Neural Designer to find patterns. As a result, we obtain a predictive model capable of detecting diseases from this information.

Activity diagram

Related solutions:

> Drug design.
> Medical prognosis.
> Medical diagnosis.