The determination of molecular features that mediate clinically aggressive phenotypes in
prostate cancer remains a major biological and clinical challenge,. Recent advances in …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Background Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive
variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises …

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death.
Determining a patient's optimal therapy is a challenge, where oncologists must select a …

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on
a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we …

Background The study of high-throughput genomic profiles from a pharmacogenomics
viewpoint has provided unprecedented insights into the oncogenic features modulating drug …

Prostate cancer is a heterogeneous disease composed of divergent molecular and
histologic subtypes, including prostate adenocarcinoma (PrAd) and neuroendocrine …

Understanding the intricacies of lethal prostate cancer poses specific challenges due to
difficulties in accurate modeling of metastasis in vivo. Here we show that NPK EYFP mice …

Prostate cancer is the second most occurring cancer in men worldwide. To better
understand the mechanisms of tumorigenesis and possible treatment responses, we …

Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …