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Received β€” 26 March 2026 ⏭

A multiomics Mendelian randomization study on PANoptosis-related genes and gastric cancer risk

βœ‡Omics in Gastric
By: Yunliang Wang Β· Yujian Xia Β· Lin Weng Β· Jin Wang Β· Wenzi Meng Β· Jiahui Zhu Β· Xin Zhao Β· Xinguo Zhu
17 March 2026 at 18:00

J Int Med Res. 2026 Mar;54(3):3000605261430163. doi: 10.1177/03000605261430163. Epub 2026 Mar 16.

ABSTRACT

ObjectiveTo explore the potential involvement of PANoptosis-related genes in gastric cancer susceptibility through multiomics analyses.MethodsSummary-data-based Mendelian randomization was performed by integrating blood-derived methylation, gene expression, and protein quantitative trait loci data with genome-wide association study results. The findings were further evaluated in The Cancer Genome Atlas cohort, followed by protein-protein interaction analysis, drug prediction, and molecular docking.ResultsSummary-data-based Mendelian randomization and colocalization analyses identified several traits suggestively associated with gastric cancer risk. Genetically predicted higher expression of apoptosis and caspase activation inhibitor (AVEN) and hepatocyte growth factor (HGF) as well as higher HGF protein levels were associated with increased risk, whereas higher levels of protein phosphatase 2 regulatory subunit B beta (PPP2R2B) appeared to be protective. Multiomics integration suggested epigenetic regulation of HGF and PPP2R2B. The Cancer Genome Atlas analysis corroborated the dysregulation of these candidates, with high AVEN expression associated with poorer survival. Protein-protein interaction and drug prediction analyses highlighted functional networks and potential therapeutics, supported by molecular docking demonstrating strong HGF-binding affinities. However, these associations did not reach statistical significance in the independent validation cohort, possibly due to limited statistical power.ConclusionsThis study identified AVEN, HGF, and PPP2R2B as potential candidate genes for gastric cancer. These findings require further validation in larger cohorts.

PMID:41840829 | DOI:10.1177/03000605261430163

Received β€” 11 March 2026 ⏭

GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Fali Wang Β· Chenglin Weng Β· Xianren Zhang Β· Siyuan Hong Β· Hui Liu Β· Suhang Wang
10 March 2026 at 12:00
arXiv:2603.06620v1 Announce Type: cross Abstract: The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods typically decouple task descriptions from graph data, generate executable code augmented by retrieval from technical documentation, and refine the code through debugging. However, we identify two key limitations in existing approaches: (i) they treat technical documentation as flat text collections and ignore its hierarchical structure, leading to noisy retrieval that degrades code generation quality; and (ii) their debugging mechanisms focus primarily on runtime errors, yet ignore more critical logical errors. To address them, we propose {\method}, an \textit{agentic hierarchical retrieval-augmented coding framework} that exploits the document hierarchy through top-down traversal and early pruning, together with a \textit{self-debugging coding agent} that iteratively refines code using automatically generated small-scale test cases. To enable comprehensive evaluation of complex graph reasoning, we introduce a new dataset, {\dataset}, covering small-scale, large-scale, and composite graph reasoning tasks. Extensive experiments demonstrate that our method achieves higher task accuracy and lower inference cost compared to baselines\footnote{The code is available at \href{https://github.com/FairyFali/GraphSkill}{\textcolor{blue}{https://github.com/FairyFali/GraphSkill}}.}.

Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yunzhong Qiu Β· Binzhu Li Β· Hao Wei Β· Shenglin Weng Β· Chen Wang Β· Zhongyi Pei Β· Mingsheng Long Β· Jianmin Wang
10 March 2026 at 12:00
arXiv:2603.06726v1 Announce Type: cross Abstract: Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series foundation models (TSFMs) effectively capture temporal dependencies, they typically underutilize cross-variate correlations and non-periodic patterns that are essential for price forecasting. Conversely, regression models excel at capturing feature interactions but are limited to future-available inputs, ignoring crucial historical drivers that are unavailable at forecast time. To bridge this gap, we propose FutureBoosting, a novel paradigm that enhances regression-based forecasts by integrating forecasted features generated from a frozen TSFM. This approach leverages the TSFM's ability to model historical patterns and injects these insights as enriched inputs into a downstream regression model. We instantiate this paradigm into a lightweight, plug-and-play framework for electricity price forecasting. Extensive evaluations on real-world electricity market data demonstrate that our framework consistently outperforms state-of-the-art TSFMs and regression baselines, achieving reductions in Mean Absolute Error (MAE) of more than 30% at most. Through ablation studies and explainable AI (XAI) techniques, we validate the contribution of forecasted features and elucidate the model's decision-making process. FutureBoosting establishes a robust, interpretable, and effective solution for practical market participation, offering a general framework for enhancing regression models with temporal context.
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