Researchers at the Research Center for Molecular Medicine of the Austrian Academy of Sciences have created knowledge-primed neural networks (KPNNs) which utilize signaling pathways and gene-regulatory networks.
Each node in a KPNN corresponds to a protein or gene, while each edge possesses a mechanistic biological interpretation. By requiring this closer correspondence, KPNNs integrate deep learning with the interpretability of biological network models, yielding tangible insights into biological systems with high prediction performance.
KPNNs are especially applicable to single-cell RNA-seq data, which is produced at massive scale with single-cell sequencing assays.
The findings illustrate the future impact that artificial intelligence (AI) and deep learning will have on mechanistic biology as the scientific community learns to add biologically interpretability to AI outcomes, the researchers say.
From Austrian Academy of Sciences
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA
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