To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.We retrospectively assessed 363 breast cancer cases
In this work, untargeted metabolomics was used to unveil the impact of a Eucalyptus (E. nitens) lipophilic outer bark extract on the metabolism of triple negative breast cancer (TNBC) and non-tumour breast cells. Integrative analysis of culture medium, intracellular polar metabolites and cellular
OBJECTIVE
To propose a new flexible and sparse classifier that results in interpretable decision support systems.
METHODS
Support vector machines (SVMs) for classification are very powerful methods to obtain classifiers for complex problems. Although the performance of these methods is consistently
Background: Lung and breast cancers are common in the world and represent major public health problems. Systemic chemotherapy is an effective way to prolong survival but it is associated with side effects. Plants are used as traditional treatments for many types of cancers, mostly in
Background and objective: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power
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