Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

371 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Data Fusion by Matrix Factorization (2013, background): “This particular approach is often preferred because of its superior predictive accuracy (Pavlidis et al., 2002; Lanckriet et al., 2004b; Gevaert et al., 2006; Tang et al., 2009; van Vliet et al., 2012), but for a given model type, it requires the development of a new inference algorithm.”
Machine learning applications in cancer prognosis and prediction (2014, neutral): “[52] BN Breast cancer 97 Clinical, microarra”
A review of microarray datasets and applied feature selection methods (2014, methodology): “Apart from the application of multivariate filter methods [34,126,121,45], the microarray problem was also treated with more complex techniques such as wrappers and embedded methods [22,63,60,97].”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, background): “In 2006 Gevaert et al. applied K2MB for the purpose of learning classifier that could be used for prognosis of breast cancer from microarray and clinical data (Gevaert et al., 2006) .”