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Received β€” 27 May 2026 ⏭

Disentangled Double Machine Learning for Accurate Causal Effect Estimation

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Guodu Xiang Β· Kui Yu Β· Yujie Wang Β· Richang Hong Β· Fuyuan Cao Β· Jiye Liang
26 May 2026 at 12:00
arXiv:2605.24808v1 Announce Type: cross Abstract: Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios. One reason is that DML estimates nuisance functions using all covariates without disentangling distinct latent factors, resulting in unreliable nuisance function estimation. Another is that imprecise nuisance estimation further introduces residual dependence between the treatment residual and the remaining outcome error, undermining the accuracy of causal effect estimates. To address these issues, in this paper, we propose Disentangled Double Machine Learning (DDML), a novel algorithm that integrates two key strategies. First, a causal role disentanglement strategy decomposes covariates into confounders, treatment-specific factors, and outcomespecific factors for enabling reliable nuisance function estimation. And second, a residual dependence orthogonalization strategy mitigates residual dependence caused by nuisance estimation errors for enhancing the precision of causal effect estimates. Experimental results on synthetic, semi-synthetic, and real-world datasets demonstrate that DDML significantly outperforms 13 state-of-the-art baseline algorithms in both MAE and RMSE.

Integrative multi-omics and experimental validation reveal UBE2C as a central hub gene and prognostic biomarker in hepatocellular carcinoma

βœ‡Omics in Hepatocellular
By: Lihuang Su Β· Ouyang Zhang Β· Yujie Wang Β· Jiaojian Lv Β· Qingxiu Liu Β· Mengchen Ma Β· Liang Xu Β· Tongtong Pan
19 May 2026 at 18:00

Int Immunopharmacol. 2026 May 19;183:116866. doi: 10.1016/j.intimp.2026.116866. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is a lethal malignancy with a high recurrence rate and limited treatment options. Ubiquitin-conjugating enzyme E2 C (UBE2C) is implicated in various cancers, yet its impact on the HCC immune landscape remains incompletely understood. Herein, hub genes in HCC were identified, by integrating co-expression networks and protein-protein interaction analyses, from the TCGA, GEO, and CPTAC databases. Their expression was analysed using a single-cell transcriptomic database and verified in HCC tissues and cell lines via quantitative reverse transcription-PCR and immunoblotting. Functional roles of UBE2C were assessed using in vitro knockdown experiments and an in vivo subcutaneous tumour model. The tumour immune microenvironment was profiled using spatial transcriptomics, RNA-seq data, and ssGSEA. A prognostic nomogram was constructed based on multivariate Cox regression. UBE2C was identified as a significantly upregulated hub gene in HCC. Single-cell RNA-seq revealed predominant expression of UBE2C in hepatocytes, with dynamic upregulation along differentiation trajectories. UBE2C knockdown suppressed proliferation, induced apoptosis, and inhibited tumour growth. Spatial transcriptomics highlighted UBE2C-high regions within proliferative niches exhibiting immunosuppressive traits-including TGFB1 enrichment, impaired CXCL9-CXCR3 signalling, and exclusion of cytotoxic T cells-which were reduced in immunotherapy responders. UBE2C expression correlated with immune checkpoint genes and specific immune cell subsets. A UBE2C-based nomogram integrating T stage and tumour stage robustly predicted patient survival, and miR-300 and miR-381-3p were identified as potential upstream regulators. These findings establish UBE2C as a key driver of HCC progression and a biomarker for prognosis and immunotherapy stratification.

PMID:42155390 | DOI:10.1016/j.intimp.2026.116866

Received β€” 11 March 2026 ⏭

UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Debabrata Mandal Β· Soumitri Chattopadhyay Β· Yujie Wang Β· Marc Niethammer Β· Praneeth Chakravarthula
10 March 2026 at 12:00
arXiv:2603.07406v1 Announce Type: cross Abstract: Universal image restoration aims to recover clean images from arbitrary real-world degradations using a single inference model. Despite significant progress, existing all-in-one restoration networks do not scale to multiple degradations. As the number of degradations increases, training becomes unstable, models grow excessively large, and performance drops across both seen and unseen domains. In this work, we show that scaling universal restoration is fundamentally limited by interference across degradations during joint learning, leading to catastrophic task forgetting. To address this challenge, we introduce a unified inference pipeline with a multi-branch mixture-of-experts architecture that decomposes restoration knowledge across specialized task-adaptable experts. Our approach enables scalable learning (over sixteen degradations), adapts and generalizes robustly to unseen domains, and supports user-controllable restoration across degradations. Beyond achieving superior performance across benchmarks, this work establishes a new design paradigm for scalable and controllable universal image restoration.
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