cs.AI, q-bio.NC updates on arXiv.org
NED-Tree: Bridging the Semantic Gap with Nonlinear Element Decomposition Tree for LLM Nonlinear Optimization Modeling Zhijing Hu · Yufan Deng · Haoyang Liu · Changjun Fan
arXiv:2604.01588v1 Announce Type: new Abstract: Automating the translation of Operations Research (OR) problems from natural language to executable models is a critical challenge. While Large Language Models (LLMs) have shown promise in linear tasks, they suffer from severe performance degradation in real-world nonlinear scenarios due to semantic misalignment between mathematical formulations and solver codes, as well as unstable information extraction. In this study, we introduce NED-Tree, a s
3 April 2026 at 12:00

NED-Tree: Bridging the Semantic Gap with Nonlinear Element Decomposition Tree for LLM Nonlinear Optimization Modeling

By: Zhijing Hu · Yufan Deng · Haoyang Liu · Changjun Fan

3 April 2026 at 12:00

arXiv:2604.01588v1 Announce Type: new Abstract: Automating the translation of Operations Research (OR) problems from natural language to executable models is a critical challenge. While Large Language Models (LLMs) have shown promise in linear tasks, they suffer from severe performance degradation in real-world nonlinear scenarios due to semantic misalignment between mathematical formulations and solver codes, as well as unstable information extraction. In this study, we introduce NED-Tree, a systematic framework designed to bridge the semantic gap. NED-Tree employs (a) a sentence-by-sentence extraction strategy to ensure robust parameter mapping and traceability; and (b) a recursive tree-based structure that adaptively decomposes complex nonlinear terms into solver-compatible sub-elements. Additionally, we present NEXTOR, a novel benchmark specifically designed for complex nonlinear, extensive-constraint OR problems. Experiments across 10 benchmarks demonstrate that NED-Tree establishes a new state-of-the-art with 72.51% average accuracy, NED-Tree is the first framework that drives LLMs to resolve nonlinear modeling difficulties through element decomposition, achieving alignment between modeling semantics and code semantics. The NED-Tree framework and benchmark are accessible in the anonymous repository https://anonymous.4open.science/r/NORA-NEXTOR.

cs.AI, q-bio.NC updates on arXiv.org
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework Qingyue Zhang · Chang Chu · Haohao Fu · Tianren Peng · Yanru Wu · Guanbo Huang · Yang Li · Shao-Lun Huang
arXiv:2601.10779v2 Announce Type: replace-cross Abstract: In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typically focus on optimizing either the source weights or the amount of transferred samples, largely neglecting their joint consideration. In this work, we propose a theoretical framework, Unified Optimization of Weights and Quantities (UOWQ), that jointly determines
3 April 2026 at 12:00

Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework

cs.AI, q-bio.NC updates on arXiv.org

By: Qingyue Zhang · Chang Chu · Haohao Fu · Tianren Peng · Yanru Wu · Guanbo Huang · Yang Li · Shao-Lun Huang

3 April 2026 at 12:00

arXiv:2601.10779v2 Announce Type: replace-cross Abstract: In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typically focus on optimizing either the source weights or the amount of transferred samples, largely neglecting their joint consideration. In this work, we propose a theoretical framework, Unified Optimization of Weights and Quantities (UOWQ), that jointly determines the optimal source weights and transfer quantities for each source task. Specifically, the framework formulates multi-source transfer learning as a parameter estimation problem based on an asymptotic analysis of a Kullback--Leibler divergence--based generalization error measure, leading to two main theoretical findings: 1) using all available source samples is always optimal when the weights are properly adjusted; 2) the optimal source weights are characterized by a principled optimization problem whose structure explicitly incorporates the Fisher information, parameter discrepancy, parameter dimensionality, and transfer quantities. Building on the theoretical results, we further propose a practical algorithm for multi-source transfer learning, and extend it to multi-task learning settings where each task simultaneously serves as both a source and a target. Extensive experiments on real-world benchmarks, including DomainNet and Office-Home, demonstrate that UOWQ consistently outperforms strong baselines. The results validate both the theoretical predictions and the practical effectiveness of our framework.

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response Hongying Zhao · Wangyang Liu · Haotian Xu · Lu Wang · Zushun Chen · Yanwu Sun · Li Wang
Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.ABSTRACTImmunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcripto
2 April 2026 at 18:00

Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Hongying Zhao · Wangyang Liu · Haotian Xu · Lu Wang · Zushun Chen · Yanwu Sun · Li Wang

2 April 2026 at 18:00

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | DOI:10.1007/s00262-026-04374-3

Omics In Lung
Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response Hongying Zhao · Wangyang Liu · Haotian Xu · Lu Wang · Zushun Chen · Yanwu Sun · Li Wang
Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.ABSTRACTImmunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcripto
2 April 2026 at 18:00

Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response

Omics In Lung

By: Hongying Zhao · Wangyang Liu · Haotian Xu · Lu Wang · Zushun Chen · Yanwu Sun · Li Wang

2 April 2026 at 18:00

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | PMC:PMC13046951 | DOI:10.1007/s00262-026-04374-3