Latest AI and machine learning research in addictions for healthcare professionals.
When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-disadvantaged subgroups. These biases tend to occur in multi-stage decision makin...
Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional neural networks (CNNs) trained on two-dimensional slices of axial T2-weighted fa...
The pathogenesis of Huntington’s disease is still incompletely understood, despite the remarkable advances in identifying the molecular effects of the...
Self-organized criticality is a hallmark of complex dynamic systems at phase transitions. Systems that operate at or near criticality have large-scale...
Adverse drug reactions (ADRs) are a major cause of clinical trial failure and post-market withdrawal, posing significant risks to public health and im...
Bionic implants are increasingly used to restore neural function yet achieving a chronically stable neural interface remains challenging. Biohybrid ne...
Understanding opioid withdrawal behaviors in preclinical models is critical to improving therapeutic approaches for opioid use disorder (OUD). However...
Foraging in the wild requires coordinated switching of critical functions, including goal-oriented navigation and context-appropriate action selection...
Hundreds of computational methods for predicting ligand binding pockets exist, but the problem of finding druggable pockets throughout the human prote...
Identifying behavioral and physiological responses to rewarding stimuli is essential for understanding positive emotional states in animals and for in...
Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and ...
Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer depe...
The prefrontal cortex (PFC) is one of the last brain regions to fully mature, making it particularly sensitive to drug use early in life. Both human a...
Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradia...
Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes connectivity and activity of neural circuits. Existing computat...
The advancement of artificial intelligence (AI) has reshaped drug discovery. AI-based models typically rely on molecular representations for predictio...
The BCL-XL anti-apoptotic protein is a clear cell Renal Cell Carcinoma (ccRCC) dependency; however, the mechanism of this dependence and its relevance...
Despite the ongoing opioid epidemic, the mortality risk of opioid initiation in patients with dementia or mild cognitive impairment (MCI) remains unde...
Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models pro...
Opioid decriminalization has taken on renewed urgency in regions grappling with high mortality and health-care costs. Traditional assessments often fo...