Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction

Advanced machine learning (ML) and deep learning (DL) methods have recently been utilized in Drug Response Prediction (DRP), and these models use the details from genomic profiles, such as extensive drug screening data and cell line data, to predict the response of drugs. Comparatively, the DL-based...

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Vydané v:	PeerJ. Computer science Ročník 10; s. e2520
Hlavní autori:	Hajim, Wesam Ibrahim, Zainudin, Suhaila, Daud, Kauthar Mohd, Alheeti, Khattab
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States PeerJ. Ltd 23.12.2024 PeerJ Inc
Predmet:	Algorithms Convolutional long short-term memory Deep learning Drug response prediction Forecasts and trends Golden eagle optimization Machine learning Neural networks Non-negativity-constrained autoencoder Spatial and temporal features Taiwan Deep learning Golden eagle optimization Spatial and temporal features Convolutional long short-term memory Non-negativity-constrained autoencoder Drug response prediction
ISSN:	2376-5992, 2376-5992
On-line prístup:	Získať plný text
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