biological result interpretation

Bridging the gap between general biomedical knowledge and actionable, testable hypotheses for specific experimental or clinical contexts remains a critical challenge. This work proposes SCENE, a novel framework that formalizes knowledge contextualization as an iterative search process through a dual-layer multi-agent architecture to deeply integrate knowledge-driven and data-driven reasoning. The upper-layer agent generates search directions and anchors relevant data patterns, while the lower-layer agent leverages knowledge graph guidance and multi-objective optimization to produce verifiable propositions that balance evidential strength with empirical support. Evaluated in real-world settings, SCENE successfully identified patient subgroups with heterogeneous treatment effects in clinical trials and discovered perturbation contexts with high target-response alignment in the LINCS L1000 study, significantly outperforming existing baselines. The generated hypotheses exhibit strong traceability, reproducibility, and expert verifiability.

Current evaluations of bioinformatics agents overemphasize answer correctness while neglecting workflow auditability and scientific credibility. This work proposes a Function–Evidence–Validation (FEV) tri-dimensional evaluation framework centered on inspectable workflow trajectories, shifting the primary focus to workflow correctness for the first time. Through systematic literature review, trajectory analysis, and cross-domain benchmark mapping, the study comprehensively analyzes 109 agent systems and 28 evaluation resources across subfields including genomics, single-cell and spatial omics, and protein science. The findings reveal that while agents perform adequately in planning and execution, they exhibit significant deficiencies in reproducibility, traceability, external validation, and prospective experimental design. This research provides both theoretical grounding and practical guidance for developing transparent, auditable next-generation bioinformatics agents.

This study addresses how spatial biologists can guide and validate complex tissue data analysis tasks executed by AI agents. Building upon the Claude Science agent, the authors employ contextual inquiry, formative pilots, and observational experiments to propose four key design directions: execution control, familiar views, source information transparency, and cross-environment accessible verification. The work reveals the epistemic mechanisms through which scientists rely on visual evidence to evaluate AI-generated results. Furthermore, it constructs a comprehensive empirical model of the analytical workflow encompassing both interactive control and verification. Ultimately, this research contributes a systematic design framework for human-AI collaborative scientific discovery, offering actionable insights into integrating intelligent agents within rigorous biological research practices.

研究通过构建生物知识图谱，评估了大型语言模型在不同信息条件下生成生物医学假设的能力，发现提供完整路径能促使模型基于证据进行推理。

Doctoral students in life sciences commonly lack formal software engineering training, hindering the development of robust, reproducible, and collaborative research software. Method: This study proposes ten pedagogical principles for research software development, establishing the first systematic framework centered on “research software pedagogy”—distinct from generic programming instruction. It integrates software engineering best practices (e.g., Git-based version control, CI/CD pipelines, unit testing, RESTful API design), learning science principles, and authentic research workflows, emphasizing the seamless embedding of automation, documentation, testing, and collaborative practices throughout the research lifecycle. Contribution/Results: The framework delivers a generalizable, plug-and-play pedagogical paradigm. Deployed across multiple Chinese universities’ life sciences PhD programs, it has demonstrably improved software deliverable quality, code reusability, and cross-team collaboration efficiency—bridging critical gaps between computational literacy and rigorous, team-based scientific software practice.

This study addresses the accessibility deficits and decision-making opacity in bioinformatics arising from an overreliance on visualization. We propose ten non-visual analysis rules that uniquely align accessibility with computational reproducibility. By leveraging text-first programming, structured metadata, and FAIR principles, this approach transforms graphical outputs into structured decision records equivalent to visual representations. Validation using single-cell RNA-seq data demonstrates that this method effectively enables analytical reconstruction in non-visual environments. Consequently, it significantly enhances research inclusivity, transparency, and auditability. Ultimately, this work establishes a novel pathway toward developing universally accessible yet rigorous paradigms for computational biology, bridging the gap between equitable access and scientific integrity.

This study addresses the challenges of translating animal experimental evidence to humans and the unclear role of artificial intelligence (AI) in supporting multi-stakeholder evidence appraisal. Through semi-structured interviews with thirteen stakeholders and requirements engineering methods, we analyzed their evidence practice challenges and needs for AI-assisted locating, screening, and extracting evidence. The findings reveal differentiated needs across diverse expertise backgrounds, clarify the value of AI in evidence retrieval, and highlight stakeholders’ cautious attitudes toward automated interpretation. Consequently, this work proposes role-sensitive design principles for AI tools, emphasizing transparency, source traceability, uncertainty communication, and human oversight. These principles establish a foundation for developing transparent and controllable AI-driven decision support systems in translational medicine.

本文提出了一种量化证据挖掘方法，旨在解决生物医学AI中自动提取的科学声明可靠性问题，通过提取结构化证据单元来提高其可信度。

This study addresses the lack of executable and verifiable knowledge representations in existing meta-analyses, which hinders the traceability and reproducibility of critical analytical decisions. To overcome this limitation, the authors propose Executable Analytical Knowledge Representation (EAKR) and introduce MetaSynDec, an agent-based framework that, for the first time, enables explicit modeling, machine-actionable execution, and closed-loop validation of meta-analytic decisions. The system leverages large language models to generate structured knowledge and validates and executes it through deterministic, schema- and contract-based services. Evaluated across 58 synthesis units, EAKR successfully constructed all units, achieved exact evidence-set consistency in 75% of cases, and produced confidence intervals overlapping with published results in 98.2% of cases—substantially outperforming direct LLM-generated approaches.

本文提出一种语义模型来表示遗传学证据，旨在解决基础科学与临床之间的差距，通过精细分类和结构对齐现有标准，适用于AI辅助的变异解读。