We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

Objective To conduct a systematic sco** review of explainable artificial intelligence (XAI)
models that use real-world electronic health record data, categorize these techniques …

For data mining tasks on high-dimensional data, feature selection is a necessary pre-
processing stage that plays an important role in removing redundant or irrelevant features …

There is substantial heterogeneity among primary prostate cancers, evident in the spectrum
of molecular abnormalities and its variable clinical course. As part of The Cancer Genome …

The cancer transcriptome is remarkably complex, including low-abundance transcripts,
many not polyadenylated. To fully characterize the transcriptome of localized prostate …

Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

Cancer-associated genetic alterations induce expression of tumour antigens that can
activate CD8+ cytotoxic T cells (CTLs), but the microenvironment of established tumours …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …