Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
The effect of Future Quest Transition Workshops on high school students [PDF]
Includes bibliographical ...
Bavlnka, Sharon
core
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
A Systematic Review of the Consequences of Stigma and Stereotype Threat for Individuals With Specific Learning Disabilities. [PDF]
Haft SL +2 more
europepmc +1 more source
Musculoskeletal humanoids exhibit rich biomechanical properties that remain insufficiently unified in prior discussions. This article systematically categorizes muscle characteristics into five properties: redundancy, independency, anisotropy, variable moment arm, and nonlinear elasticity, and analyzes their combined effects on control.
Kento Kawaharazuka +2 more
wiley +1 more source
Efficacy of a Task-Oriented Intervention for Children with a Dual Diagnosis of Specific Learning Disabilities and Developmental Coordination Disorder: A Pilot Study. [PDF]
Rameckers EAA +3 more
europepmc +1 more source
Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson +3 more
wiley +1 more source
What I Wish My Instructor Knew: How Active Learning Influences the Classroom Experiences and Self-Advocacy of STEM Majors with ADHD and Specific Learning Disabilities. [PDF]
Pfeifer MA, Cordero JJ, Stanton JD.
europepmc +1 more source
This article introduces an input sparsity‐aware computing‐in‐memory macro featuring novel bidirectional conversion‐skippable analog‐to‐digital converters. By dynamically adjusting resolution based on element‐level sparsity, the architecture skips redundant most significant bit and least significant bit conversions.
Choongseok Song +2 more
wiley +1 more source

