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Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

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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

Metabolic Effects of a Eucalyptus Bark Lipophilic Extract on Triple Negative Breast Cancer and Non-Tumor Breast Epithelial Cells

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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

White box radial basis function classifiers with component selection for clinical prediction models.

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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

Potency of Combining Eucalyptus camaldulensis subsp. camaldulensis with Low-Dose Cisplatin in A549 Human Lung Adenocarcinomas and MCF-7 Breast Adenocarcinoma

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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

White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment

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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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