Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 6,341 to 6,350 of 205,745 articles

A Foundation Model for the Cancer Genome

bioRxiv
Cancer is a disease of the genome, in which somatic mutations and copy-number alterations determine tumour identity, clinical behaviour, and response to therapy. Consortium-scale sequencing has profiled hundreds of thousands of tumours, yet clinical ... read more 

Ultra-efficient High Resolution 3D Reconstruction of Spatial Omics Data with Neural Transcriptomic Field

bioRxiv
Biological tissues are inherently three-dimensional (3D) ecosystems where spatial architecture dictates cellular function. While spatial omics technologies have revolutionized molecular profiling, they are largely restricted to isolated two-dimension... read more 

Eyewire II - A connectomic resource for resolving cell types and circuits of the mouse retina

bioRxiv
Comprehensive wiring diagrams from electron microscopy (EM) are a powerful tool to understand the inner workings of the brain. The retina is an easily accessible part of the brain that performs complex visual computations. Its thin, layered structure... read more 

Assessing and Optimizing Low-Frequency Somatic Mutation Detection: A Multi-Platform High-Throughput Sequencing Perspective

bioRxiv
The availability of multiple commercial short-read sequencing platforms necessitates systematic cross-platform performance comparisons, particularly for challenging applications such as low-frequency somatic mutation detection. Here, a large-scale ta... read more 

GeneKnow: AI-powered literature synthesis for gene-context analysis

bioRxiv
Interpreting gene function in specific biological contexts is essential for biomedical research, yet manual literature review is labor-intensive. We developed GeneKnow, a source-grounded framework that uses generative AI models within a controlled hy... read more 

UMITIC: An unsupervised framework for the joint characterization of cellular phenotypes and spatial neighborhoods in multiplex and hyperplex immunofluorescence imaging data

bioRxiv
Multiplexed imaging technologies enable the simultaneous measurement of dozens of protein markers while preserving context, providing a high-resolution view of tissue organization schemes. However, extracting meaningful insights from these high-dimen... read more 

Decoding Cognitive States from fMRI Using Classical Machine Learning and Temporal Dynamics Analysis: An Interpretable Approach Using the Human Connectome Project

bioRxiv
We propose a rigorous and reproducible methodology for analyzing functional MRI data, aimed at: (1) demonstrate their efficiency in classifying task-induced brain states with a limited amount of data, (2) present a methodology to identify brain regio... read more 

Forecasting novel therapeutic development in biomedical research

bioRxiv
Early identification of promising drug research topics is challenging yet crucial for the scientific community to accelerate the development of novel therapeutics. In this work, we leverage large-scale public data from the biomedical literature to ex... read more 

Mesoscopic cortical activities associated with pupil-linked perceptions inferred via explainable machine learning

bioRxiv
Pupil dilation reflects arousal-related neural processes and is closely linked to sensory perception, attention, and cognitive state, but the mesoscopic cortical dynamics that accompany stimulus-evoked dilation remain unclear. Here, we combined simul... read more 

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

bioRxiv
Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental... read more