The promising role of new molecular biomarkers in prostate cancer: From coding and non-coding genes to artificial intelligence approaches

AP Alarcón-Zendejas, A Scavuzzo… - Prostate cancer and …, 2022 - nature.com
Background Risk stratification or progression in prostate cancer is performed with the
support of clinical-pathological data such as the sum of the Gleason score and serum levels …

Genomics and artificial intelligence: prostate cancer

EY Wong, TN Chu, SS Ladi-Seyedian - Urologic Clinics, 2024 - urologic.theclinics.com
Management of patients with prostate cancer (PCa) is often challenging due to the
difference in the biology of cancer between patients and the various genetic alterations …

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

N Schaduangrat, N Anuwongcharoen… - Journal of …, 2023 - Springer
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent
feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic …

Dual inhibitory action of a novel AKR1C3 inhibitor on both full-length AR and the variant AR-V7 in enzalutamide resistant metastatic castration resistant prostate …

M Kafka, F Mayr, V Temml, G Möller, J Adamski, J Höfer… - Cancers, 2020 - mdpi.com
The expanded use of second-generation antiandrogens revolutionized the treatment
landscape of progressed prostate cancer. However, resistances to these novel drugs are …

Applications of machine learning to predict cisplatin resistance in lung cancer

Y Gao, Q Lyu, P Luo, M Li, R Zhou… - International journal of …, 2021 - Taylor & Francis
Purpose Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and
small cell lung cancer, has the highest incidence and cancer-related mortality worldwide …

Prediction of fingerling biomass with deep learning

MCB Pache, DA Sant'Ana, JVA Rozales… - Ecological …, 2022 - Elsevier
Organic nitrogenous nutrients present in proteins like fish are important and indispensable
for cell formation, growth, and reproduction of living beings. The search for quality protein is …

[HTML][HTML] Prediction of secondary testosterone deficiency using machine learning: A comparative analysis of ensemble and base classifiers, probability calibration, and …

MT Novaes, OLF de Carvalho, PHG Ferreira… - Informatics in Medicine …, 2021 - Elsevier
Testosterone is the most important male sex hormone, and its deficiency brings many
physical and mental harms. Efficiently identifying individuals with low testosterone is crucial …

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

A Bazarkin, A Morozov, A Androsov, H Fajkovic… - Current urology …, 2024 - Springer
Abstract Purpose of Review The aim of the systematic review is to assess AI's capabilities in
the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for …

Prediction of protein–ligand interaction based on sequence similarity and ligand structural features

D Karasev, B Sobolev, A Lagunin, D Filimonov… - International journal of …, 2020 - mdpi.com
Computationally predicting the interaction of proteins and ligands presents three main
directions: the search of new target proteins for ligands, the search of new ligands for …

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

WI Hajim, S Zainudin, KM Daud, K Alheeti - PeerJ Computer Science, 2024 - peerj.com
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to
develop personalized healthcare services, particularly in drug response prediction (DRP) for …