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Health
BayesiaLab, an invaluable tool for pharmaceutical and medical research«Through the «unsupervised’ analysis of our databank carried out by BayesiaLab, we were able to determine correlations that we would never have imagined, . or even searched for! This is a really innovative and highly interesting characteristic of the Bayesia method. »
The power of BayesiaLab’s learning algorithms enables researchers to handle a very large quantity of data and can lead them to new, extremely significant lines of thought. This is why BayesiaLab is now used by teaching hospitals and large pharmaceutical laboratories.
«BayesiaLab is adaptable. It is a detection source in its own right. Its capacity for finding scenarios and associating them with data analysis is apt to lead us towards new and particularly significant lines of thought.»
«BayesiaLab is highly adapted to our needs, particularly in the analysis of the complex decision making problems we are faced with daily.»
BayesiaLab can also be used as a treatment simulation tool. A doctor can use it during his consultations to calculate the probabilities of successful treatment, or the recurrence of an illness, for each patient, according to the variables characterizing him.
Find out more about BayesiaLab »
A few application examples
- Microarray analysis with Bayesian Networks In this study, we turn to the field of cancer classification by means of microarray analysis. Microarray analysis is a technique for gene expression profiling of cell samples. Expression profiles indicate which genes are currently active among thousands of genes.
- Breast Cancer Diagnostics with Bayesian Networks Our white paper reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before.
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Difficult intubations analysis
Scata 2007 (London)
Prediction of difficult intubation (DI) is crucial during pre anaesthesia assessment of a patient. Many criterions are used to predict DI with different performances. In this case study, we show how BayesiaLab, through its learning algorithms, allows to quickly discovering unknown probabilistic relationships between variables and enhance prediction.
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Salmonella isolation
20th International Pig Veterinary Society Congress, 2008, Durban (South Africa)
Identification of factors associated with Salmonella isolation on pork carcasses via bayesian networks.
- Biocomputing transcriptome analysis Bioinformatics with BayesiaLab.
- Health trajectory analysis Prediction of medical needs with BayesiaLab.
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