AIMC Topic: Receptors, Estrogen

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Efficient prediction of progesterone receptor interactome using a support vector machine model.

International journal of molecular sciences
Protein-protein interaction (PPI) is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, p...

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HER2- early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decisi...

Subtyping-Directed Precision Treatment Refines Traditional One-Size-Fits-All Therapy for HR+/HER2- Breast Cancer.

Cancer research
UNLABELLED: The standard approach of using one-size-fits-all endocrine therapy for hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancers has faced significant challenges because of variations in tr...

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2.

Journal of hazardous materials
The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organisms. In this study, we developed novel strategies for multi-species screening of estrogen receptor ...

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland)
PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused ...

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Lab on a chip
Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine therapy (ET). Recent studies identify the tumor microenvironment (TME) as having a key role in endoc...

Advances in research on receptor heterogeneity in breast cancer liver metastasis.

Bioscience trends
Breast cancer liver metastasis (BCLM) presents a critical challenge in breast cancer treatment and has substantial epidemiological and clinical significance. Receptor status is pivotal in managing both primary breast cancer and its liver metastases. ...

Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems ...

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor.

Current computer-aided drug design
BACKGROUND: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterat...