Using a machine learning method, this study aimed to identify unique causal networks of
genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network …

Background The causal networks among genes that are commonly expressed in osteoblasts
and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we …

Background Even though we have established a few risk factors for metastatic breast cancer
(MBC) through epidemiologic studies, these risk factors have not proven to be effective in …

Understanding the mechanisms of gene regulation during breast cancer is one of the most
difficult problems among oncologists because this regulation is likely comprised of complex …

Aim: Method: This research presents a model combining machine learning (ML) techniques
and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and …

Identification of conserved co-expression networks is a useful tool for clustering groups of
genes enriched for common molecular or cellular functions. The relative importance of …

In breast cancer, metastases are relatively widely distributed, with the most common sites
being bone, regional lymph nodes, lung, liver, and brain. The detailed mechanism of organ …

Understanding and preventing the development of distant metastases is the most important
aim in research and treatment of malignant tumors, including breast cancer. In patients with …

Background Breast cancer is one of the most common endocrine cancers among females
worldwide. Distant metastasis of breast cancer is causing an increasing number of breast …

Due to the large variability in survival times between cancer patients and the plethora of
genes on microarrays unrelated to outcome, building accurate prediction models that are …