Results 161 to 170 of about 16,305 (230)

Artificial intelligence-derived transition zone PSA density as a triage tool to reduce unnecessary prostate systematic biopsies in MRI-negative men. [PDF]

Insights Imaging
Shang J, Wu J, Deng R, Shang M, Wu P, Qiu J, Zhou J, Cai L, Wang X, Gong K, Liu Y.   +10 more
europepmc   +1 more source

Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning. [PDF]

Sci Adv
Zhang X, Qu S, Lu F, Wang J, Tian Z, Gu S, Zhang Y, Knoll A, Gao S, Chen G, Jiang C.   +10 more
europepmc   +1 more source

Development and validation of a clinical nomogram based on lasso-logistic regression for predicting prostate cancer with PSA 4-20.0 ng/mL: a retrospective study. [PDF]

Front Endocrinol (Lausanne)
Ying M, Wang L, Yang M, Ke M, Sun L.
europepmc   +1 more source

Clinical validation of iXip index in avoiding unnecessary prostate biopsy: results from a prospective multicenter study involving 426 patients [PDF]

, 2017
Antonio, Garbelio, Filiberto, Zattoni, Gianluca, Giannarini, Giorgio, Fassina, Lucio, Laurini, Mario, Plebani, Stefano, Guazzieri, Zani, Danilo, Zani, Danilo   +8 more
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Identification of prostate cancer by urinary DLX1/HOXC6 expression in Chinese population with prostate-specific antigen levels of 4-10 ng/mL. [PDF]

Sci Rep
Zhang D, Xu J, Wang J, Jiang J.
europepmc   +1 more source

Unveiling the potential of apigenin and kaempferol against colon cancer: an integrated network pharmacology and docking approach. [PDF]

Front Bioinform
Selvakumar A, Chandran PA, Shraddha S, Dharshini LCP, Perumal S, Karuppasamy R, Mandal AKA.   +6 more
europepmc   +1 more source

Developing an interpretable clinical-radiomics machine learning model using whole transition zone MRI analysis for improving diagnosis of transition zone prostate cancer. [PDF]

Front Oncol
Yang J, Jiang N, Zhang Y, Yang L, Xia L, Ren Y, Xu H, Li Z, Wang J, Cui F.   +9 more
europepmc   +1 more source

Association between platelet-to-lymphocyte ratio and high risk for prostate cancer among middle-aged and older US men. [PDF]

Sci Rep
Chang Y, Wang W, Tao J, Zheng Z, Zhou J.
europepmc   +1 more source

Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients. [PDF]

Front Oncol
Zhu B, Jiang H, Zhang C, Wang Q, Yang L, Yang H, Li R, Li J, Pang X, Zheng Y, Yan L, Wang Y, Bai Y.   +12 more
europepmc   +1 more source

An Application of Markov-switching Model to stock Returns Analysis [PDF]

Monika Kosko
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