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Accelerating Drug Discovery with HyperLab: An Easy-to-Use AI-Driven Platform

HyperLab, developed by HITS, is a web-based, AI-driven drug discovery platform designed to increase research efficiency for experimental drug discovery researchers. The platform features an intuitive user interface and experience (UI/UX), enabling researchers without specialized expertise in AI or computational methods to readily generate essential discovery outcomes. By employing a Structure-Base...

The alternated brain states in resting state after immoral decisions

Immoral decisions, which engage both cognitive control and reward system, bring both cognitive and neural consequences. However, how dishonesty has an impact on the brain’s dynamic alterations is still unclear. In this work, we attempt to understand the impact of immoral decisions on the brain states’ dynamics using both resting-state and task-state fMRI data collected before, during, and after an...

Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance

Post-marketing surveillance is crucial for drug safety, yet the tools of pharmacovigilance rely solely on text-based data that may limit the applicabi...

A study on edge devices for image classification of the Tasmanian devil (Sarcophilus harrisii) for vaccine delivery

A target-specific bait dispenser is required for oral bait vaccination of the endangered Tasmanian devil (Sarcophilus harrisii) against the deadly dev...

Oyster: a neural network for modelling genomic sequences that enables exact position-specific k-mer contributions

Genomic functions arise from nucleotide sequences and their overlapping k-mers – subsequences whose contributions depend on their composition, positio...

From minutes to bounds: A probabilistic UV-C control and a shape-only morphological fingerprint for postharvest Colletotrichum

Postharvest losses in high-value horticultural crops such as cacao and coffee are often driven by Colletotrichum spp. and other latent fruit pathogens...

Spatially distinct chromatin compaction states predict neoadjuvant chemotherapy resistance in Triple Negative Breast Cancer

Organisation and dynamics of chromatin play a key role in regulation of cell state and function. In cancer, chromatin plasticity is known to be import...

A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction

Genomic prediction and design require models that integrate local sequence features with long-range regulatory dependencies spanning hundreds of kilob...

Polli-markers: spectral and chemical biomarkers for detecting cryptic early plant pollination responses

Pollination is essential for plant reproduction, ecosystem resilience and human health. Yet, our capability to map pollination service delivery in rea...

Probing Large Language Model Hidden States for Adverse Drug Reaction Knowledge

Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. However, these representations are diffic...

A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes

Emerging evidence indicates an elevated risk of post-concussion musculoskeletal (MSK) injuries in collegiate athletes; however, identifying athletes a...

CONORM: Context-Aware Entity Normalization for Adverse Drug Event Detection

Adverse drug events (ADEs) are a critical aspect of patient safety and pharmacovigilance, with significant implications for patient outcomes and publi...

Performance of an artificial intelligence foundation model for prostate radiotherapy segmentation

Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with min...

Automated Detection of Faciobrachial Dystonic Seizures Related Events in LGI1 Autoimmune Encephalitis Patients with Wearables

To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated w...

Expanding cholera serosurveillance to vaccinated populations

Mass oral cholera vaccination campaigns targeted at subnational areas with high incidence are central to global cholera elimination efforts. Serologic...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning

Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study ...

Machine learning-based calculation of neurovascular compression surface area correlates with post-microvascular decompression pain outcomes for trigeminal neuralgia

Machine learning-generated segmentations of the trigeminal nerve and nearby blood vessels have the potential to quantify the magnitude of neurovascula...

Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use - A Scoping Review

Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains ...

High Sensitivity in Spontaneous Intracranial Hemorrhage Detection from Emergency Head CT Scans Using Meta-Learning Approach

Spontaneous intracranial hemorrhages have a high disease burden. Due to increasing medical imaging, new technological solutions for assisting in image...

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