AIMC Topic: Hormones
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Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.
Biomedical engineering online
Dec 23, 2024
PURPOSE: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic resonance imaging (MRI) and the clinical marker prostate-specific antigen (PSA). It addresses the li...
HormoNet: a deep learning approach for hormone-drug interaction prediction.
BMC bioinformatics
Feb 28, 2024
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is ...
Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.
NEJM evidence
Jun 29, 2023
BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We us...
Bioinspired, ingestible electroceutical capsules for hunger-regulating hormone modulation.
Science robotics
Apr 26, 2023
The gut-brain axis, which is mediated via enteric and central neurohormonal signaling, is known to regulate a broad set of physiological functions from feeding to emotional behavior. Various pharmaceuticals and surgical interventions, such as motilit...
Automated classification of estrous stage in rodents using deep learning.
Scientific reports
Oct 21, 2022
The rodent estrous cycle modulates a range of biological functions, from gene expression to behavior. The cycle is typically divided into four stages, each characterized by distinct hormone concentration profiles. Given the difficulty of repeatedly s...
An overview of deep learning applications in precocious puberty and thyroid dysfunction.
Frontiers in endocrinology
Oct 20, 2022
In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult...
Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration.
Sensors (Basel, Switzerland)
May 26, 2022
Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activi...
Deep learning-based predictive identification of neural stem cell differentiation.
Nature communications
May 10, 2021
The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is...
Predicting cross-tissue hormone-gene relations using balanced word embeddings.
Bioinformatics (Oxford, England)
Oct 14, 2022
MOTIVATION: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have foster...
Identification of hormone binding proteins based on machine learning methods.
Mathematical biosciences and engineering : MBE
Mar 22, 2019
The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequis...
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