ArticlePloS one2021
On transformative adaptive activation functions in neural networks for gene expression inference.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs.PloS one · 2023Article
- Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence.PNAS nexus · 2022Article
- On tower and checkerboard neural network architectures for gene expression inference.BMC genomics · 2020Article
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
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Authors and funding
2 authors.
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Abstract
Gene expression profiling was made more cost-effective by the NIH LINCS program that profiles only ∼1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D-GEX method employs neural networks to infer the entire profile. However, the original D-GEX can be significantly improved. We propose a novel transformative adaptive activation function that improves the gene expression inference even further and which generalizes several existing adaptive activation functions. Our improved neural network achieves an average mean absolute error of 0.1340, which is a significant improvement over our reimplementation of the original D-GEX, which achieves an average mean absolute error of 0.1637. The proposed transformative adaptive function enables a significantly more accurate reconstruction of the full gene expression profiles with only a small increase in the complexity of the model and its training procedure compared to other methods.
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