Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule

As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests. However, its greedy multi-layer learning procedure is prone to overfitting, limiting model effectiveness and generalizability. This paper presents an optimized Deep Forest, featuring learnable, layerwise data augmentation policy schedules. Specifically, We introduce the Cut Mix for Tabular data (CMT) augmentation technique to mitigate overfitting and develop a population-based search algorithm to tailor augmentation intensity for each layer. Additionally, we propose to incorporate outputs from intermediate layers into a checkpoint ensemble for more stable performance. Experimental results show that our method sets new state-of-the-art (SOTA) benchmarks in various tabular classification tasks, outperforming shallow tree ensembles, deep forests, deep neural network, and AutoML competitors. The learned policies also transfer effectively to Deep Forest variants, underscoring its potential for enhancing non-differentiable deep learning modules in tabular signal processing

The c-Myc-regulated lncRNA NEAT1 and paraspeckles modulate imatinib-induced apoptosis in CML cells

Abstract Chronic myeloid leukemia (CML) is a clonal disease characterized by the presence of the constitutively active tyrosine kinase BCR-ABL oncoprotein. Although BCR-ABL is crucially important for pathogenesis and treatment response, it is thought that some additional factors might be involved in the regulation of these processes. Aberrant expression of long noncoding RNAs (lncRNAs) has recently been identified to be involved in various diseases including cancer, suggesting that lncRNAs may play a role in BCR-ABL-mediated CML. In this study, we found that nuclear-enriched abundant transcript 1 (NEAT1), a lncRNA essential for the formation of nuclear body paraspeckles, is significantly repressed in primary CML cells. NEAT1 expression could be restored by inhibiting BCR-ABL expression or its kinase activity in K562 cells. We also demonstrated that NEAT1 is regulated by c-Myc. Knockdown of NEAT1 could promote imatinib (IM)-induced apoptosis, and we demonstrated that the NEAT1-binding paraspeckle protein splicing factor proline/glutamine-rich (SFPQ) is required for NEAT1-mediated apoptosis in K562 cells. RNA-seq analysis revealed that SFPQ regulates cell growth and death pathway-related genes, confirming its function in IM-induced apoptosis. Collectively, these results assign a biological function to the NEAT1 lncRNA in CML apoptosis and may lead to fuller understanding of the molecular events leading to CML