AIMC Topic: Dopamine
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Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.
ACS chemical neuroscience
Jun 25, 2018
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein...
Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Feb 15, 2018
Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suf...
Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.
PloS one
Jan 25, 2016
At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. H...
Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning.
Journal of neurophysiology
Oct 14, 2015
The basal ganglia are a collection of subcortical nuclei thought to underlie a wide variety of vertebrate behavior. Although a great deal is known about the functional and physiological properties of the basal ganglia, relatively few models have been...
Identifying the Basal Ganglia network model markers for medication-induced impulsivity in Parkinson's disease patients.
PloS one
Jun 4, 2015
Impulsivity, i.e. irresistibility in the execution of actions, may be prominent in Parkinson's disease (PD) patients who are treated with dopamine precursors or dopamine receptor agonists. In this study, we combine clinical investigations with comput...
A spiking neural network based on the basal ganglia functional anatomy.
Neural networks : the official journal of the International Neural Network Society
Mar 24, 2015
We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and consideri...
Portable and intelligent ratio fluorometry and colorimetry for dual-mode detection of dopamine based on B, N-codoped carbon dots and machine learning.
Talanta
Nov 1, 2025
A dual-mode approach was developed for dopamine (DA) assay based on boron (B) and nitrogen (N) co-doped carbon dots (B, N-CDs). This platform enabled highly sensitive and specific detection of DA in biological samples through collaborative ratio fluo...
A Chemistry-Informed Generative Deep Learning Approach for Enhancing Voltammetric Neurochemical Sensing in Living Mouse Brain.
Journal of the American Chemical Society
May 21, 2025
Exploring the time-resolved dynamics of neurochemicals is essential for deciphering neuronal functions, intercellular communication, and neurophysiological or pathological mechanisms. However, the complex interplay among neurochemicals between neuroc...
The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.
Cerebral cortex (New York, N.Y. : 1991)
Feb 5, 2025
This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic resonance data from 551 children in the Autism Brain Imaging Data Exchange database were analyzed, focu...
Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.
Journal of the American Medical Informatics Association : JAMIA
Feb 16, 2024
OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa d...
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