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Received — 12 January 2026 ⏭ Favourites

Benchmarking LLM-based Agents for Single-cell Omics Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Liu · Lu Zhou · Xiawei Du · Ruikun He · Rongbo Shen · Yixue Li
12 January 2026 at 13:00
arXiv:2508.13201v2 Announce Type: replace-cross Abstract: The surge in multimodal single-cell omics data exposes limitations in traditional, manually defined analysis workflows. AI agents offer a paradigm shift, enabling adaptive planning, executable code generation, traceable decisions, and real-time knowledge fusion. However, the lack of a comprehensive benchmark critically hinders progress. We introduce a novel benchmarking evaluation system to rigorously assess agent capabilities in single-cell omics analysis. This system comprises: a unified platform compatible with diverse agent frameworks and LLMs; multidimensional metrics assessing cognitive program synthesis, collaboration, execution efficiency, bioinformatics knowledge integration, and task completion quality; and 50 diverse real-world single-cell omics analysis tasks spanning multi-omics, species, and sequencing technologies. Our evaluation reveals that Grok-3-beta achieves state-of-the-art performance among tested agent frameworks. Multi-agent frameworks significantly enhance collaboration and execution efficiency over single-agent approaches through specialized role division. Attribution analyses of agent capabilities identify that high-quality code generation is crucial for task success, and self-reflection has the most significant overall impact, followed by retrieval-augmented generation (RAG) and planning. This work highlights persistent challenges in code generation, long-context handling, and context-aware knowledge retrieval, providing a critical empirical foundation and best practices for developing robust AI agents in computational biology.
Received — 7 January 2026 ⏭ Favourites

PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

✇MRD
By: Yu Wang · Yong-Bo Xiang · Xiao-Wei Chen · Tao Zhang · Jian-Yang Wang · Wen-Yang Liu · Lei Deng · Lu-Hua Wang · Shu-Geng Gao · Nan Bi
6 January 2026 at 19:00

Mil Med Res. 2026 Jan 6;12(1):94. doi: 10.1186/s40779-025-00679-z.

ABSTRACT

BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.

METHODS: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.

RESULTS: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.

CONCLUSIONS: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.

PMID:41491583 | PMC:PMC12771999 | DOI:10.1186/s40779-025-00679-z

Received — 10 December 2025 ⏭ Favourites

A Large-Scale Multimodal Dataset and Benchmarks for Human Activity Scene Understanding and Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyang Jiang · Mu Yuan · Xiang Ji · Bufang Yang · Zeyu Liu · Lilin Xu · Yang Li · Yuting He · Liran Dong · Wenrui Lu · Zhenyu Yan · Xiaofan Jiang · Wei Gao · Hongkai Chen · Guoliang Xing
9 December 2025 at 13:00
arXiv:2512.07136v1 Announce Type: cross Abstract: Multimodal human action recognition (HAR) leverages complementary sensors for activity classification. Beyond recognition, recent advances in large language models (LLMs) enable detailed descriptions and causal reasoning, motivating new tasks: human action understanding (HAU) and human action reasoning (HARn). However, most LLMs, especially large vision language models (LVLMs), struggle with non-RGB modalities such as depth, IMU, and mmWave due to the lack of large-scale data-caption resources. Existing HAR datasets mainly provide coarse data-label annotations, which are insufficient to capture fine-grained action dynamics needed for HAU and HARn. We consider two ground-truth pair types: (1) data label (discrete category) and (2) data caption (textual description). Naively generating captions from labels often lacks logical and spatiotemporal consistency. We introduce CUHK-X, a large-scale multimodal dataset and benchmark suite for HAR, HAU, and HARn. CUHK-X contains 58,445 samples covering 40 actions performed by 30 participants across two indoor environments. To improve caption consistency, we propose a prompt-based scene creation method that leverages LLMs to generate logically connected activity sequences, followed by human validation. CUHK-X includes three benchmarks with six evaluation tasks. Experiments report average accuracies of 76.52% (HAR), 40.76% (HAU), and 70.25% (HARn). CUHK-X aims to enable the community to apply and develop data-intensive learning methods for robust, multimodal human activity analysis. Project page and code: https://openaiotlab.github.io/CUHK-X/ and https://github.com/openaiotlab/CUHK-X.
Received — 9 December 2025 ⏭ Favourites

Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yangxiao Cai · Ruiyin Li · Peng Liang · Mojtaba Shahin · Zengyang Li
8 December 2025 at 13:00
arXiv:2511.08475v2 Announce Type: replace-cross Abstract: As the complexity of Software Engineering (SE) tasks continues to escalate, Multi-Agent Systems (MASs) have emerged as a focal point of research and practice due to their autonomy and scalability. Furthermore, through leveraging the reasoning and planning capabilities of Large Language Models (LLMs), the application of LLM-based MASs in the field of SE is garnering increasing attention. However, there is no dedicated study that systematically explores the design of LLM-based MASs, including the Quality Attributes (QAs) on which designers mainly focus, the design patterns used by designers, and the rationale guiding the design of LLM-based MASs for SE tasks. To this end, we conducted a study to identify the QAs that LLM-based MASs for SE tasks focus on, the design patterns used in the MASs, and the design rationale for the MASs. We collected 94 papers on LLM-based MASs for SE tasks as the source. Our study shows that: (1) Code Generation is the most common SE task solved by LLM-based MASs among ten identified SE tasks, (2) Functional Suitability is the QA on which designers of LLM-based MASs pay the most attention, (3) Role-Based Cooperation is the design pattern most frequently employed among 16 patterns used to construct LLM-based MASs, and (4) Improving the Quality of Generated Code is the most common rationale behind the design of LLM-based MASs. Based on the study results, we presented the implications for the design of LLM-based MASs to support SE tasks.
Received — 3 December 2025 ⏭ Favourites

Human Decision-making is Susceptible to AI-driven Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sahand Sabour · June M. Liu · Siyang Liu · Chris Z. Yao · Shiyao Cui · Xuanming Zhang · Wen Zhang · Yaru Cao · Advait Bhat · Jian Guan · Wei Wu · Rada Mihalcea · Hongning Wang · Tim Althoff · Tatia M. C. Lee · Minlie Huang
2 December 2025 at 13:00
arXiv:2502.07663v3 Announce Type: replace Abstract: AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.
Received — 26 November 2025 ⏭ Favourites

Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell transcriptomic analysis

✇Omics in Hepatocellular
By: Yang Li · Jun Shi · Aiqing Zhao · Jing Huang · Lei Dai
25 November 2025 at 19:00

Discov Oncol. 2025 Nov 25;16(1):2162. doi: 10.1007/s12672-025-04010-z.

ABSTRACT

Liver hepatocellular carcinoma (LIHC), a predominant form of primary hepatic malignancy, demonstrates a progressively escalating global incidence, imposing substantial health and economic burdens on patients and society. Early diagnosis remains challenging, often resulting in late-stage detection, which limits the efficacy of current therapeutic strategies. This study systematically examines the transcriptional signatures of apoptosis-associated and cholesterol metabolic pathways in LIHC, providing insights into its underlying mechanisms and identifying potential prognostic markers. We employed multi-omics and machine learning to evaluate gene expression variations and construct a prognostic risk scoring model. This study identified apoptosis- and cholesterol metabolism-related differentially expressed genes (ACMRDEGs). Importantly, LASSO regression analysis identified six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, and CYP7A1) as critical prognostic biomarkers, demonstrating significant correlation with overall survival (OS). Furthermore, immune cell infiltration analysis indicated significant differences in 12 immune cell types within LIHC microenvironment, underscoring the immune system's involvement in disease progression. cholesterol and alcohol metabolism pathways were significantly enriched among hub gene modules, as quantified by multiple gene enrichment analyses. Single-cell analysis identified six major cell types, providing a deeper understanding of the cellular heterogeneity within LIHC. In summarize, this study presents the first integrated apoptosis-cholesterol metabolic pathway-based six-gene prognostic model for LIHC, validated for robustness across multiple cohorts, which may facilitate personalized therapeutic strategies and refined risk assessment in clinical practice.

PMID:41288805 | PMC:PMC12647489 | DOI:10.1007/s12672-025-04010-z

Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell transcriptomic analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yang Li · Jun Shi · Aiqing Zhao · Jing Huang · Lei Dai
25 November 2025 at 19:00

Discov Oncol. 2025 Nov 25;16(1):2162. doi: 10.1007/s12672-025-04010-z.

ABSTRACT

Liver hepatocellular carcinoma (LIHC), a predominant form of primary hepatic malignancy, demonstrates a progressively escalating global incidence, imposing substantial health and economic burdens on patients and society. Early diagnosis remains challenging, often resulting in late-stage detection, which limits the efficacy of current therapeutic strategies. This study systematically examines the transcriptional signatures of apoptosis-associated and cholesterol metabolic pathways in LIHC, providing insights into its underlying mechanisms and identifying potential prognostic markers. We employed multi-omics and machine learning to evaluate gene expression variations and construct a prognostic risk scoring model. This study identified apoptosis- and cholesterol metabolism-related differentially expressed genes (ACMRDEGs). Importantly, LASSO regression analysis identified six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, and CYP7A1) as critical prognostic biomarkers, demonstrating significant correlation with overall survival (OS). Furthermore, immune cell infiltration analysis indicated significant differences in 12 immune cell types within LIHC microenvironment, underscoring the immune system's involvement in disease progression. cholesterol and alcohol metabolism pathways were significantly enriched among hub gene modules, as quantified by multiple gene enrichment analyses. Single-cell analysis identified six major cell types, providing a deeper understanding of the cellular heterogeneity within LIHC. In summarize, this study presents the first integrated apoptosis-cholesterol metabolic pathway-based six-gene prognostic model for LIHC, validated for robustness across multiple cohorts, which may facilitate personalized therapeutic strategies and refined risk assessment in clinical practice.

PMID:41288805 | DOI:10.1007/s12672-025-04010-z

Received — 18 November 2025 ⏭ Favourites

MedDCR: Learning to Design Agentic Workflows for Medical Coding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiyang Zheng · Islam Nassar · Thanh Vu · Xu Zhong · Yang Lin · Tongliang Liu · Long Duong · Yuan-Fang Li
18 November 2025 at 13:00
arXiv:2511.13361v1 Announce Type: new Abstract: Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems.
Received — 3 November 2025 ⏭ Favourites

MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Zhang · Zekun Guo · Yingce Xia · Peiran Jin · Shufang Xie · Tao Qin · Xiang-Yang Li
3 November 2025 at 13:00
arXiv:2510.27671v1 Announce Type: new Abstract: Structure-based drug design (SBDD), which maps target proteins to candidate molecular ligands, is a fundamental task in drug discovery. Effectively aligning protein structural representations with molecular representations, and ensuring alignment between generated drugs and their pharmacological properties, remains a critical challenge. To address these challenges, we propose MolChord, which integrates two key techniques: (1) to align protein and molecule structures with their textual descriptions and sequential representations (e.g., FASTA for proteins and SMILES for molecules), we leverage NatureLM, an autoregressive model unifying text, small molecules, and proteins, as the molecule generator, alongside a diffusion-based structure encoder; and (2) to guide molecules toward desired properties, we curate a property-aware dataset by integrating preference data and refine the alignment process using Direct Preference Optimization (DPO). Experimental results on CrossDocked2020 demonstrate that our approach achieves state-of-the-art performance on key evaluation metrics, highlighting its potential as a practical tool for SBDD.
Received — 8 September 2025 ⏭ Favourites

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇Omics In Lung
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng
5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Received — 31 July 2025 ⏭ Favourites

Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ricardo D'Oliveira Albanus · Xiaoshan Zhang · Zeping Zhao · Henry J Taylor · Xuming Tang · Yuling Han · Peter Orchard · Arushi Varshney · Tuo Zhang · Nandini Manickam · Michael R Erdos · Narisu Narisu · Leland Taylor · Xiaxia Saavedra · Xinyi Liu · Aaron Zhong · Bo Li · Ting Zhou · Ali Naji · Chengyang Liu · Francis S Collins · Stephen C J Parker · Shuibing Chen
30 July 2025 at 18:00

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

Received — 29 July 2025 ⏭ Favourites

Fatty acid-binding proteins in cancers

✇(Multiomics OR Omics) AND (Pancreatic)
By: Long Wu · Guang-Ling Ou · Wei Zhang · Hua-Xing Ma · Xiao-Yun Li · Yun-Huan Zhen · Huan Wu · Kun Cao · Hai-Yang Li
28 July 2025 at 18:00

Int J Surg. 2025 Jul 15. doi: 10.1097/JS9.0000000000003049. Online ahead of print.

ABSTRACT

Fatty acid-binding proteins (FABPs) are intracellular lipid chaperones with molecular weights of approximately 14-15 kDa. By binding and transporting fatty acids and lipid-related molecules, FABPs precisely regulate metabolic pathways, signal transduction, and gene expression, playing a central role in cancer initiation and progression. The 11 identified subtypes (FABP1-FABP12; FABP11 is identical to FABP3) exhibit tissue-specific expression and influence tumor progression through metabolic reprogramming, immune microenvironment modulation, and therapy resistance. Metabolically, FABPs enhance fatty acid uptake, β-oxidation, and synthesis, meeting the high proliferative demands of tumors. In immune regulation, FABP4+ macrophages secrete IL-6 to suppress T cell activity, while FABP6 downregulates MHC-I molecule expression to reduce CD8+ T cell infiltration, fostering an immunosuppressive microenvironment. Regarding therapy resistance, FABP4 enhances mitochondrial β-oxidation to reduce apoptosis in ovarian cancer, and FABP5 promotes chemoresistance in HCC via the HIF-1α pathway. Functional heterogeneity exists among subtypes: FABP7 drives glioblastoma stem cell migration via RXRα signaling, while FABP5 exhibits context-dependent roles, promoting HCC progression but suppressing colorectal cancer (CRC) through mTOR-mediated autophagy. Clinically, FABPs serve as diagnostic biomarkers and therapeutic targets. However, challenges such as insufficient target specificity, cross-cancer heterogeneity, and normal tissue toxicity remain. Future studies should integrate multi-omics and single-cell technologies to elucidate cell-specific mechanisms and develop precise combination therapies for clinical translation.

PMID:40717587 | DOI:10.1097/JS9.0000000000003049

Received — 14 July 2025 ⏭ Favourites

A data-intelligence-intensive bioinformatics copilot system for large-scale omics research and scientific insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yang Liu · Rongbo Shen · Lu Zhou · Qingyu Xiao · Jiao Yuan · Yixue Li
10 July 2025 at 18:00

Brief Bioinform. 2025 Jul 2;26(4):bbaf312. doi: 10.1093/bib/bbaf312.

ABSTRACT

Advancements in high-throughput sequencing technologies and artificial intelligence (AI) offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of AI technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here, we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human researchers to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human researchers. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves overall state-of-the-art performance across all tasks, while showcasing exceptional task completeness. Furthermore, on application to constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a recursive, multilevel annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories.

PMID:40639418 | PMC:PMC12245162 | DOI:10.1093/bib/bbaf312

Received — 23 June 2025 ⏭ Favourites

Advancements in liquid biopsy for breast Cancer: Molecular biomarkers and clinical applications

✇MRD
By: Peng Qiu · Xiaopeng Yu · Fushuang Zheng · Xi Gu · QianQiu Huang · Ke Qin · Yueting Hu · Bowen Liu · Tianming Xu · Tao Zhang · Guanglei Chen · Yang Liu
20 June 2025 at 18:00

Cancer Treat Rev. 2025 Jun 14;139:102979. doi: 10.1016/j.ctrv.2025.102979. Online ahead of print.

ABSTRACT

Breast cancer is characterized by significant molecular heterogeneity; therefore, there are distinct clinical features, treatment modalities, and prognostic outcomes across its various molecular subtypes. In the era of precision medicine, liquid biopsy has emerged as a convenient and minimally invasive technique capable of dynamically representing the comprehensive tumor gene spectrum. This review systematically elaborates the clinical value of liquid biopsy as a breakthrough tool for precision diagnosis and treatment in breast cancer through dynamic detection of key biomarkers, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and non-coding RNA (ncRNA). Specific genetic mutations and methylation signatures in ctDNA can be applied to early breast cancer screening, minimal residual disease monitoring, and tracking drug resistance mechanisms. CTCs enumeration (≥1/7.5 mL in early-stage cancer or ≥ 5/7.5 mL in metastatic cancer) and PD-L1 expression levels demonstrate direct correlations with prognostic stratification and the efficacy of immunotherapy. As the specificity and sensitivity of liquid biopsy continue to improve, personalized treatment strategies, informed by biomarker analysis and targeted precision therapies, have unveiled new avenues of hope for patients with breast cancer. However, several challenges persist in the practical application of liquid biopsy. Despite persistent challenges, such as insufficient standardization and difficulties in resolving low-abundance variants, future advancements should focus on multi-omics integration and AI-driven technological breakthroughs to overcome bottlenecks in clinical translation. This review summarizes cutting-edge liquid biopsy technologies for identifying clinically significant molecular biomarkers, focusing on discussing critical challenges in the strategies to advance precision oncology applications for optimized treatment guidance and disease surveillance in breast cancer.

PMID:40540857 | DOI:10.1016/j.ctrv.2025.102979

Received — 24 January 2025 ⏭ Favourites

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations

✇Cell
By: Jason J. Liu · Beatrice Borsari · Yunyang Li · Susanna X. Liu · Yuan Gao · Xin Xin · Shaoke Lou · Matthew Jensen · Diego Garrido-Martín · Terril L. Verplaetse · Garrett Ash · Jing Zhang · Matthew J. Girgenti · Walter Roberts · Mark Gerstein
19 December 2024 at 08:00
Complex disorders require precise strategies for their characterization. AI-based digital phenotypes from biosensors can be used to predict psychiatric disorders and identify GWAS loci.
Received — 24 December 2024 ⏭ Favourites

Author Correction: π-HuB: the proteomic navigator of the human body

✇Nature - Issue - nature.com science feeds
By: Fuchu He · Ruedi Aebersold · Mark S. Baker · Xiuwu Bian · Xiaochen Bo · Daniel W. Chan · Cheng Chang · Luonan Chen · Xiangmei Chen · Yu-Ju Chen · Heping Cheng · Ben C. Collins · Fernando Corrales · Jürgen Cox · Weinan E · Jennifer E. Van Eyk · Jia Fan · Pouya Faridi · Daniel Figeys · George Fu Gao · Wen Gao · Zu-Hua Gao · Keisuke Goda · Wilson Wen Bin Goh · Dongfeng Gu · Changjiang Guo · Tiannan Guo · Yuezhong He · Albert J. R. Heck · Henning Hermjakob · Tony Hunter · Narayanan Gopalakrishna Iyer · Ying Jiang · Connie R. Jimenez · Lokesh Joshi · Neil L. Kelleher · Ming Li · Yang Li · Qingsong Lin · Cui Hua Liu · Fan Liu · Guang-Hui Liu · Yansheng Liu · Zhihua Liu · Teck Yew Low · Ben Lu · Matthias Mann · Anming Meng · Robert L. Moritz · Edouard Nice · Guang Ning · Gilbert S. Omenn · Christopher M. Overall · Giuseppe Palmisano · Yaojin Peng · Charles Pineau · Terence Chuen Wai Poon · Anthony W. Purcell · Jie Qiao · Roger R. Reddel · Phillip J. Robinson · Paola Roncada · Chris Sander · Jiahao Sha · Erwei Song · Sanjeeva Srivastava · Aihua Sun · Siu Kwan Sze · Chao Tang · Liuju
23 December 2024 at 08:00

Nature, Published online: 23 December 2024; doi:10.1038/s41586-024-08555-x

Author Correction: π-HuB: the proteomic navigator of the human body

Spatial epigenome–transcriptome co-profiling of mammalian tissues

✇Nature - Issue - nature.com science feeds
By: Di Zhang · Yanxiang Deng · Petra Kukanja · Eneritz Agirre · Marek Bartosovic · Mingze Dong · Cong Ma · Sai Ma · Graham Su · Shuozhen Bao · Yang Liu · Yang Xiao · Gorazd B. Rosoklija · Andrew J. Dwork · J. John Mann · Kam W. Leong · Maura Boldrini · Liya Wang · Maximilian
15 March 2023 at 08:00

Nature, Published online: 15 March 2023; doi:10.1038/s41586-023-05795-1

The authors present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications and gene expression on the same tissue section at near-single-cell resolution.
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