AIMC Topic: Receptors, Estrogen

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Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Ecotoxicology and environmental safety Jul 13, 2023
Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the...
Artificial Intelligence Risk Assessment Endocrine Disruptors Receptors, Estrogen Humans
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Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques.

IEEE journal of translational engineering in health and medicine Feb 6, 2023
OBJECTIVE: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subty...
Humans Female Immunohistochemistry Breast Neoplasms Receptors, Estrogen Deep Learning Ki-67 Antigen
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Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.

European radiology Jan 29, 2022
OBJECTIVE: To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).
Contrast Media Mammography Humans Receptors, Estrogen Retrospective Studies Female Breast Neoplasms Deep Learning
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Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

European journal of cancer (Oxford, England : 1990) Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
Humans Machine Learning Aged Aged, 80 and over Young Adult Adult Prognosis Retrospective Studies Electronic Health Records Female Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone Disease Progression Antineoplastic Combined Chemotherapy Protocols Follow-Up Studies Natural Language Processing Survival Rate Breast Neoplasms Middle Aged
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Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.

PLoS computational biology Sep 24, 2020
Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a ...
Cell Line Receptors, Estrogen Machine Learning Algorithms Estrogens Humans
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Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer.

Scientific reports Aug 21, 2020
Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC...
Recurrence Neoplasms, Hormone-Dependent Breast Neoplasms Biomarkers, Tumor Logistic Models Sequence Analysis, RNA Estrogens Carcinoma Datasets as Topic Receptors, Estrogen Humans Machine Learning Prognosis Female
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Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

Laboratory investigation; a journal of technical methods and pathology Aug 10, 2020
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula...
Protein Binding Mice Computational Biology Models, Statistical Databases, Chemical Endocrine Disruptors Receptors, Estrogen Machine Learning Algorithms Animals Deep Learning Humans
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A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Scientific reports Jul 2, 2020
Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient...
Middle Aged Adolescent ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Receptor, ErbB-2 Carcinoma, Ductal, Breast Aged, 80 and over Young Adult Adult Breast Neoplasms Tomography, X-Ray Computed Humans Female Machine Learning Aged Prognosis Receptors, Estrogen Receptors, Progesterone Carcinoma, Lobular
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Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

Scientific reports Apr 29, 2020
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorith...
Breast Neoplasms Biomarkers, Tumor Middle Aged Deep Learning Female Aged Adult Tissue Array Analysis Image Processing, Computer-Assisted Humans Progesterone Receptor, ErbB-2 Receptors, Estrogen
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Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.

Scientific reports Nov 28, 2019
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of m...
Breast Neoplasms Female Humans ErbB Receptors Aged Middle Aged Machine Learning Aged, 80 and over Tomography, X-Ray Computed Neoplasm Invasiveness Receptors, Estrogen Adult Neoplasm Grading Ki-67 Antigen
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