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Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. Plain Language Summary: Antarctic sea ice has changed significantly since 2016, leading to a higher demand for sea ice forecasts. However, forecasting Antarctic sea ice has not received enough attention. Limited observations and lack of understanding of ice‐related physical mechanisms result in significant errors in sea ice predictions in dynamical models, particularly at the subseasonal timescale. We utilized a deep‐learning model to predict Antarctic sea ice at this timescale to fill this gap. Results showed that the deep‐learning model performed skillfully 1–8 weeks in advance, with the Weddell Sea being the best‐predicted region and the West Pacific being the worst. Furthermore, our study found that the model significantly outperformed mainstream dynamic models and a conventional statistical model. These findings build a foundation for developing more advanced prediction models at high resolutions for operational applications. Key Points: A deep‐learning model outperforms dynamic models, filling a subseasonal Antarctic sea ice prediction gap by bypassing physical mechanismsUnlike a Markov model that predicts time series of fixed spatial patterns, our model can extract ice synchronized spatiotemporal evolutionsThe deep‐learning model captures climate signals in sea ice, delivering the best prediction in fall season and the Weddell Sea region [ABSTRACT FROM AUTHOR]

Unsupervised clustering is an essential step in identifying cell types from single‐cell RNA sequencing (scRNA‐seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single‐cell molecular heterogeneity. In particular, a silhouette coefficient‐based indicator is developed to determine the optimization direction of the bi‐objective function. In addition, a hierarchical autoencoder is employed to project the high‐dimensional data onto multiple low‐dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi‐objective fruit fly optimization algorithm is designed to prune dynamically the low‐quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA‐seq datasets and one large real scRNA‐seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post‐transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms. [ABSTRACT FROM AUTHOR]