Cargando…
Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion
Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical explanation on the model determination and predictio...
Autores principales: | Tang, Hui, Yu, Xiangtian, Liu, Rui, Zeng, Tao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921615/ https://www.ncbi.nlm.nih.gov/pubmed/35106553 http://dx.doi.org/10.1093/bib/bbab584 |
Ejemplares similares
-
Confusion2Vec: towards enriching vector space word representations with representational ambiguities
por: Gurunath Shivakumar, Prashanth, et al.
Publicado: (2019) -
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
por: Smaili, Fatima Zohra, et al.
Publicado: (2018) -
Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification
por: Ghnemat, Rawan, et al.
Publicado: (2023) -
Generating Mesh Representations of VecGeom Solids
por: Topak, Murat
Publicado: (2019) -
GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings
por: Zhong, Xiaoshi, et al.
Publicado: (2020)