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Received — 10 December 2025 ⏭ Favourites

WisPaper: Your AI Scholar Search Engine

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li Ju · Jun Zhao · Mingxu Chai · Ziyu Shen · Xiangyang Wang · Yage Geng · Chunchun Ma · Hao Peng · Guangbin Li · Tao Li · Chengyong Liao · Fu Wang · Xiaolong Wang · Junshen Chen · Rui Gong · Shijia Liang · Feiyan Li · Ming Zhang · Kexin Tan · Jujie Ye · Zhiheng Xi · Shihan Dou · Tao Gui · Yuankai Ying · Yang Shi · Yue Zhang · Qi Zhang
9 December 2025 at 13:00
arXiv:2512.06879v1 Announce Type: cross Abstract: Researchers struggle to efficiently locate and manage relevant literature within the exponentially growing body of scientific publications. We present \textsc{WisPaper}, an intelligent academic retrieval and literature management platform that addresses this challenge through three integrated capabilities: (1) \textit{Scholar Search}, featuring both quick keyword-based and deep agentic search modes for efficient paper discovery; (2) \textit{Library}, a customizable knowledge base for systematic literature organization; and (3) \textit{AI Feeds}, an intelligent recommendation system that automatically delivers relevant new publications based on user interests. Unlike existing academic tools, \textsc{WisPaper} provides a closed-loop workflow that seamlessly connects literature discovery, management, and continuous tracking of research frontiers. Our multilingual and multidisciplinary system significantly reduces the time researchers from diverse backgrounds spend on paper screening and management, enabling them to focus on their core research activities. The platform is publicly accessible and serves researchers across academia and industry.
Received — 27 November 2025 ⏭ Favourites
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • A Brief History of Digital Twin Technology Yunqi Zhang · Kuangyu Shi · Biao Li
    arXiv:2511.20695v1 Announce Type: new Abstract: Emerging from NASA's spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time data streams and capable of bidirectional interaction. In medicine, digital twin integrates imaging, biosensors, and computational models to generate patient-specific s
     
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A Brief History of Digital Twin Technology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunqi Zhang · Kuangyu Shi · Biao Li
27 November 2025 at 13:00
arXiv:2511.20695v1 Announce Type: new Abstract: Emerging from NASA's spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time data streams and capable of bidirectional interaction. In medicine, digital twin integrates imaging, biosensors, and computational models to generate patient-specific simulations that support diagnosis, treatment planning, and drug development. Representative applications include cardiac digital twin for predicting arrhythmia treatment outcomes, oncology digital twin for tracking tumor progression and optimizing radiotherapy, and pharmacological digital twin for accelerating drug discovery. Despite rapid progress, major challenges, including interoperability, data privacy, and model fidelity, continue to limit widespread clinical integration. Emerging solutions such as explainable AI, federated learning, and harmonized regulatory frameworks offer promising pathways forward. Looking ahead, advances in multi-organ digital twin, genomics integration, and ethical governance will be essential to ensure that digital twin shifts healthcare from reactive treatment to predictive, preventive, and truly personalized medicine.
Received — 14 November 2025 ⏭ Favourites

LLM4AD: Large Language Models for Autonomous Driving - Concept, Review, Benchmark, Experiments, and Future Trends

✇cs.AI, q-bio.NC updates on arXiv.org
By: Can Cui · Yunsheng Ma · Sung-Yeon Park · Zichong Yang · Yupeng Zhou · Juanwu Lu · Juntong Peng · Jiaru Zhang · Ruqi Zhang · Lingxi Li · Yaobin Chen · Jitesh H. Panchal · Amr Abdelraouf · Rohit Gupta · Kyungtae Han · Ziran Wang
13 November 2025 at 13:00
arXiv:2410.15281v4 Announce Type: replace-cross Abstract: With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving technology. Driven by their natural language understanding and reasoning capabilities, LLMs have the potential to enhance various aspects of autonomous driving systems, from perception and scene understanding to interactive decision-making. In this paper, we first introduce the novel concept of designing Large Language Models for Autonomous Driving (LLM4AD), followed by a review of existing LLM4AD studies. Then, we propose a comprehensive benchmark for evaluating the instruction-following and reasoning abilities of LLM4AD systems, which includes LaMPilot-Bench, CARLA Leaderboard 1.0 Benchmark in simulation and NuPlanQA for multi-view visual question answering. Furthermore, we conduct extensive real-world experiments on autonomous vehicle platforms, examining both on-cloud and on-edge LLM deployment for personalized decision-making and motion control. Next, we explore the future trends of integrating language diffusion models into autonomous driving, exemplified by the proposed ViLaD (Vision-Language Diffusion) framework. Finally, we discuss the main challenges of LLM4AD, including latency, deployment, security and privacy, safety, trust and transparency, and personalization.
Received — 25 May 2025 ⏭ Favourites

Role of the "inflammation-immunity-metabolism" network in non-small cell lung cancer: a multi-omics analysis

✇Omics In Lung
By: Jingqi Zhang · Liping Lin · Wenyuan Li · Jing Guo
21 May 2025 at 18:00

Discov Oncol. 2025 May 21;16(1):847. doi: 10.1007/s12672-025-02692-z.

ABSTRACT

Lung cancer remains one of the leading causes of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for 85% of cases worldwide. NSCLC pathogenesis and progression are intricately linked to inflammatory stimuli, immune evasion, and metabolic reprogramming. In this study, the impact of inflammation, immunity, and metabolism on NSCLC was investigated by a Mendelian randomization analysis taking 91 inflammatory factors, 731 immune cells, and 1400 metabolites as exposures, and the FinnGen database NSCLC cohort (ncases = 5315, ncontrol = 314,193) was the outcome. A number of metabolites, inflammatory proteins, and immune cells were identified as potentially associated with NSCLC based on mendelian randomization analysis. Validation in the UK Biobank database lung cancer cohort (ncases = 2671, ncontrols = 372,016) further confirmed the inhibitory role of the metabolite N-acetyl-aspartyl-glutamate (NAAG) on lung cancer. Subsequently, single-cell and protein-protein interaction analyses identified inflammatory protein expression patterns in NSCLC, distribution ratios of immune cells in NSCLC. Subsequent multi-omics network analysis showed key interaction nodes between NAAG and inflammatory proteins. These findings enhance the understanding of the roles of inflammation, immunity, and metabolism in NSCLC occurrence and progression, offering potential targets and strategies for further research on its treatment and management.

PMID:40397292 | PMC:PMC12095725 | DOI:10.1007/s12672-025-02692-z

Received — 1 February 2025 ⏭ Favourites

A deep learning framework for <em>in silico</em> screening of anticancer drugs at the single-cell level

✇(Multiomics OR Omics) AND (Pancreatic)
By: Peijing Zhang · Xueyi Wang · Xufeng Cen · Qi Zhang · Yuting Fu · Yuqing Mei · Xinru Wang · Renying Wang · Jingjing Wang · Hongwei Ouyang · Tingbo Liang · Hongguang Xia · Xiaoping Han · Guoji Guo
28 January 2025 at 19:00

Natl Sci Rev. 2024 Dec 10;12(2):nwae451. doi: 10.1093/nsr/nwae451. eCollection 2025 Feb.

ABSTRACT

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

PMID:39872221 | PMC:PMC11771446 | DOI:10.1093/nsr/nwae451

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