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Found 4 papers, 1 papers with code
Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps
In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset.
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1. 96 (log-scale IC50 values).
