Evaluating Agentic Bioinformatics through Function, Evidence, and Validation

Phuc Pham; Truong-Son Hy

REVIEW 2 major objections 4 minor 224 references

Evaluating Agentic Bioinformatics through Function, Evidence, and Validation

T0 review · 2 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read AI biology agents should be judged by workflow correctness, not final answers alone.

desk verdict Useful evaluation scaffold for agentic bioinformatics; the empirical V-stage coding is looser than the stated gate, so treat the counts as indicative. read the letter →

arxiv 2607.27556 v1 pith:64NS3EAL submitted 2026-07-30 cs.AI cs.MA

Phuc Pham , Truong-Son Hy This is my paper · ORCID

classification cs.AIcs.MA

keywords agenticbioinformaticsworkflowcorrectnessFunction–Evidence–Validationvalidationstagesevidencegroundingreplayabilitylargelanguagemodelagentsscientificaccountability

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that large language model agents in bioinformatics are being evaluated by the wrong yardstick. Fluent outputs, successful tool calls, and benchmark scores say little about whether the process that produced them was scientifically defensible. The authors propose that the inspectable workflow trajectory—the sequence of objectives, data, decisions, tools, artifacts, and evidence—should be the primary unit of analysis, and they introduce a Function–Evidence–Validation (FEV) framework to measure it. Applying FEV to 109 systems, they find that planning and tool use have advanced far ahead of replayability, provenance, and prospective empirical testing: 94 of 109 systems meet the replayability gate, but only 7 have any prospective experimental validation. The conclusion is that the field should shift from final-answer correctness to workflow correctness.

What carries the argument

The Function–Evidence–Validation (FEV) framework. Function records what the system demonstrably does (planning, coordination, tool execution, state and trace maintenance, repair, verification). Evidence records traceable sources (literature, knowledge bases, measurements, software outputs, model outputs, experimental observations). Validation is a five-stage cumulative ladder from illustrative output (V0) to demonstrated execution (V1), replayable computation (V2), scientifically evaluated computation (V3), and prospective empirical evaluation (V4). The ladder is the load-bearing instrument: it separates 'it ran' from 'it can be replayed' from 'it was scientifically tested.'

What would settle it

Re-code the 109 mapped systems from the paper's own tables using only the explicit V2 minimum (inputs, parameters, dependencies, intermediate artifacts, and execution traces all present). If a stricter coder assigns substantially fewer than 94 systems to V2—for instance, because 'public code and instructions' is treated as insufficient without traceable execution artifacts—the claimed replayability gap and the V-stage distribution would shift together.

Watch

Extended reading notes

Core claim

The central claim is normative: agentic bioinformatics should be evaluated through workflow correctness rather than final-answer correctness alone. The paper operationalizes this as three non-interchangeable properties—demonstrated workflow operations (Function F1–F6), traceable support for actions and claims (Evidence E1–E6), and use-case-specific cumulative assurance (Validation V0–V4)—and maps 109 systems plus 28 benchmarks across six biological domains. The empirical finding is lopsided progress: 94 of 109 systems reach the V2 replayability gate, but only 7 reach V4 prospective empirical testing, and closed-loop empirical refinement appears in a single mapped system. If the paper is righ

Load-bearing premise

The headline numbers (94 of 109 at V2, 7 at V4) rest on the authors applying their own stated V2 criteria—identifiable inputs, parameters, dependencies, intermediate artifacts, and execution traces—consistently across 109 heterogeneous papers, and that coding has not been checked by independent raters.

Editorial extensions

If this is right

Benchmarks in agentic bioinformatics should grade trajectories—tool calls, parameters, artifacts, failure recovery—alongside final answers, rather than treating endpoint accuracy as the whole score.
Published system claims should carry an explicit V-stage and qualifiers (benchmark, expert, statistics, robustness, external, prospective, closed-loop), so a reader can see what assurance a given use case actually has.
The replayability gap (94 of 109 at V2, 7 at V4) implies that most current systems are inspectable but not empirically tested; funding and evaluation efforts should move toward prospective, claim-aligned experiments.
A minimum reporting standard for agentic bioinformatics papers would follow from FEV: document workflow scope, models, tools, parameters, environments, artifacts, failures, approval points, and the validation stage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If FEV became a reporting norm, the field's progress measures would shift from 'can it answer' to 'can it be audited'—a change that would likely re-rank many systems and reduce the prestige of broad-but-untested agents.
The V-ladder could be extended with a V5 for closed-loop empirical refinement at scale, since the paper counts only one such system; the distinction between one-shot prospective testing (P) and feedback-driven cycles (C) is likely to become central as wet-lab integration grows.
A testable extension would be an inter-rater reliability study of FEV coding: if independent coders disagree widely on V-stage assignments, the framework needs tighter operational definitions before it can serve as a community standard.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

You should know two things about arXiv:2607.27556. One: the FEV framework — separating Function, Evidence, and Validation for agentic bioinformatics workflows — is a genuinely useful contribution and the paper is worth reading. Two: the empirical V-stage coding is sloppier than the stated criteria, so the headline counts (109 systems, 86% at V2+, 69 at V3, 7 at V4) should be read as indicative, not exact.

What's actually new: treating the inspectable workflow trajectory as the unit of analysis, not architecture or final answer. The paper makes the case that benchmark accuracy and tool-call fluency don't establish scientific credibility, and that's well argued. The V0–V4 validation stages with qualifiers (B, H, S, R, X, P, C) give the field a common vocabulary. The cross-domain synthesis is a solid piece of work; the accounting checks out (109 + 28 − 9 = 128, and the V-stage sum is correct). The finding that only 7 of 109 systems reach prospective empirical evaluation, and only 1 has closed-loop refinement, is a useful corrective to the hype.

The soft spots are real but not fatal. The stress-test is right: Table S5 requires V2 to include identifiable inputs, parameters, dependencies, intermediate artifacts, and execution traces. Yet several table entries — AI-HOPE, HEAL-KGGen, SwiftDossier — are assigned V2 on "public code" or "executable analyses" without the required artifacts and traces. Since V3 subsumes V2, the 69 V3 count inherits the same slack. No inter-rater reliability check or actual replay verification is reported. So the "replayability gap" is overstated; with a strict reading, more systems drop to V1. The normative argument survives, but the empirical map needs tightening. Also, the abstract bundles the weak replayability gap with the strong prospective-testing gap; those should be separated. Minor: reference [49] is duplicated as [50].

Who benefits: anyone building or evaluating bioinformatics agents, and anyone writing benchmarks. The framework is a good scaffold even if the assignments need calibration.

Recommendation: send it to peer review. A serious referee should ask for a coding log, a stricter and consistent V2 gate, and softened claims about the replayability gap. The core idea is sound and the resource is timely.

Referee Report

2 major / 4 minor

Summary. The paper introduces the Function–Evidence–Validation (FEV) framework for evaluating agentic bioinformatics systems. Function records demonstrable workflow operations (F1–F6), Evidence records traceable sources supporting actions and claims (E1–E6), and Validation records cumulative assurance stages (V0–V4) with orthogonal qualifiers. The authors apply FEV to 109 system entries and 28 benchmark resources, representing 128 unique publications, and present a cross-domain synthesis showing that planning and tool-mediated execution are common while replayability, external validation, and prospective empirical testing are less well established. They conclude that agentic bioinformatics should be assessed through workflow correctness rather than final-answer correctness alone.

Significance. The paper makes a timely and useful conceptual contribution. The FEV framework separates operational capability, evidentiary support, and validation assurance in a way that is more explicit than most existing reviews, and the supplementary tables provide an unusually detailed audit trail of 109 systems. The distinction between using empirical data as evidence and prospectively testing an agent-generated output is particularly valuable, as is the cumulative V0–V4 gate. The accounting is internally consistent (109 + 28 − 9 = 128 unique publications), and the authors are transparent about the review's scope and limitations. The main risk is that the quantitative V-stage synthesis is not calibrated: several V2/V3 assignments appear more lenient than the stated criteria, and no inter-rater reliability or replay check is reported. This weakens the specific numeric claims but does not invalidate the normative core, which would only be strengthened if replayability is even rarer than reported.

major comments (2)

[§S2 Tables S8, S9, S23; Table S5; §S1] The V2 gate is defined in Table S5 as requiring identifiable inputs, parameters, dependencies, intermediate artifacts, AND execution traces. Several assignments use a looser bar. AI-HOPE (Table S8) is coded V2 [S] on 'executable analyses on identifiable retrospective datasets,' with no listed scripts, parameter records, logs, or traces. HEAL-KGGen (Table S9) is coded V2 [B] because 'public code, requirements, test data, graph files, and instructions support replay'; instructions and graph files are not execution traces. SwiftDossier (Table S23) is coded V2 [H,R] from 'executable retrieval and analysis artifacts' without the required intermediate artifacts/traces. Since V3 subsumes V2, the 69 V3 entries inherit this slack. Section S1 reports no inter-rater reliability or calibration exercise and no actual replay check. This undermines quantitative statements such as 'most systems are clas
[§2 and §S1; Figures S3–S5] The eligibility boundary is deliberately broad, including 'agent-adjacent' systems, and §S1 states that aggregate analyses include both unless otherwise stated. However, the main quantitative synthesis does not report the full-agentic subset separately. Entries such as ChatNT (Table S7), VibeGen (Table S20), and ORI (Table S21) are coded as agent-adjacent predictive or model–laboratory loops, yet they contribute to the same FEV prevalence and V-stage distribution as full multi-agent workflow systems. The claim that 'planning and tool-mediated execution have advanced' across agentic bioinformatics is therefore hard to interpret. Please report the full-agentic-only distribution or provide a sensitivity analysis showing that the qualitative conclusions are unchanged when agent-adjacent entries are excluded.

minor comments (4)

[References] References 49 and 50 are identical (Huang et al., 'Autonomous biomedical research with an artificial intelligence agent'). Please consolidate or distinguish them. References 117 and 118 also appear to be two versions of BioMaster and should be cross-referenced explicitly.
[Figure S3b] The zero count for V0 is partly an artifact of the eligibility filter that excludes purely conversational systems. Add a note clarifying that V0 is retained on the complete scale but that no system meeting the inclusion criteria was assigned to it.
[Abstract and §2] The term 'workflow correctness' is used in the abstract and conclusion but never given a compact definition in the body. A brief formal definition early in Section 2 would help readers understand the exact relationship between FEV and workflow correctness.
[Figure S5b] The 'observed gap' score (1 − share) measures absence of reporting, not absence of capability. Since the paper codes only reported capabilities, consider relabeling this as a 'reporting gap' or adding an explicit sentence that unreported capabilities were treated as not demonstrated.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FEV is a proposed analytical framework applied to external systems; no prediction reduces to a fit or self-citation.

full rationale

The paper introduces the Function–Evidence–Validation framework as a normative analytical lens rather than deriving it from data. The central claim — 'agentic bioinformatics should be assessed through workflow correctness rather than final-answer correctness alone' — is an argument supported by the framework's definitions and by a structured mapping of 109 external systems and 28 benchmarks. There are no equations, no fitted parameters, and no quantity predicted from a subset of the same data. The V-stage assignments are coding judgments about other groups' systems, not outputs of a model fitted to those systems; even if some assignments are lenient relative to Table S5 (e.g., AI-HOPE, HEAL-KGGen, SwiftDossier receive V2 without the table's required intermediate artifacts and execution traces), that is a measurement-reliability concern about the review's empirical summary, not a circular reduction of the paper's conclusions to its inputs. The only mildly self-referential element is Table S2, where the authors' own review is the only row marked with full checks on the feature checklist that they themselves defined; this is a positioning table rather than load-bearing evidence for the normative claim, and it does not constitute circularity in the sense of a prediction or derivation that reduces to its inputs. The manuscript also explicitly disclaims that 'Unreported or insufficiently documented capabilities were not coded' (Supplementary Section S2), a transparent limitation rather than a circular step. No load-bearing self-citations or imported uniqueness theorems appear. The paper is therefore not circular; its principal risks are external validity and coding reliability, not derivation circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

FEV is a taxonomy/framework, not a physical entity, so invented_entities is empty. The paper's measurable contributions rest on two hand-set coding instruments (V-stage thresholds; the agentic/agent-adjacent eligibility boundary) applied by the authors without inter-rater reliability, plus two stated domain assumptions: that the workflow trajectory is the right unit of analysis, and that unreported capabilities can be treated as absent. The framework itself (F1–F6, E1–E6, V0–V4, qualifiers) is defined operationally in Tables S3–S5 rather than fitted to data.

free parameters (2)

V-stage assignment thresholds (V0–V4)
Hand-set cumulative gates defined in Table S5 and applied by the authors to 109 systems. No inter-rater reliability is reported, and several assignments (AI-HOPE, HEAL-KGGen, SwiftDossier) use support weaker than the stated gate, so the V-stage distribution in Figure S3b is partly a function of coder judgment rather than a measured property of the systems.
Eligibility boundary: 'agentic or agent-adjacent'
Inclusion threshold defined in Section S1 ('one or more substantive workflow-level capabilities beyond unconstrained conversational response generation'). Subjective application determines which systems enter the denominator of every proportion in Figures S3–S5.

assumptions (3)

domain assumption The inspectable workflow trajectory, not architecture or final output, is the correct primary unit of analysis for agentic bioinformatics.
Core normative premise of the paper, introduced in the Introduction ('we treat the inspectable workflow trajectory, rather than the architecture or final answer alone, as the primary unit of analysis') and operationalized by the entire FEV framework; the paper argues for it but does not test it.
domain assumption Unreported or insufficiently documented capabilities can be treated as absent for the purpose of the field-wide map.
Stated in Section S2: 'Unreported or insufficiently documented capabilities were not coded.' The gap scores in Figure S5 therefore measure reporting as a proxy for capability; a preprint with thin methods is coded lower regardless of actual system behavior.
domain assumption The structured sample of 128 publications is adequate to support temporal claims such as 'planning and tool-mediated execution have advanced more rapidly than...'.
The paper discloses that counts 'describe the structured review sample rather than the complete literature' (Section S1), yet the abstract states the 'advanced more rapidly' finding as a field-level conclusion; the inference from a convenience sample of indexed plus backward-cited papers to the field's trajectory is assumed, not validated.

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Cite this review

Pith. "Pith review of Evaluating Agentic Bioinformatics through Function, Evidence, and Validation." pith.science (2026). https://pith.science/paper/64NS3EAL

@misc{pith2026260727556,
  author       = {Pith},
  title        = {Pith review of: Evaluating Agentic Bioinformatics through Function, Evidence, and Validation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/64NS3EAL}},
  note         = {Machine review of arXiv:2607.27556}
}

read the original abstract

Large language model agents increasingly plan, execute, and interpret biological analyses, yet fluent responses, successful tool calls, and benchmark performance alone do not establish scientific credibility. Existing reviews primarily organize biological agents by application, architecture, and agentic capability, but do not jointly operationalize the accountability of agent-generated workflows. We address this gap by treating the inspectable workflow trajectory, rather than architecture or final output alone, as the primary unit of analysis. We introduce the Function--Evidence--Validation (FEV) framework, which separates demonstrated workflow operations, traceable support for actions and claims, and use-case-specific validation. Using FEV, we map 109 agentic or agent-adjacent systems and 28 benchmark or evaluation resources, representing 128 unique publications across genomics, single-cell and spatial omics, protein science, drug discovery, computational pathology, and general bioinformatics automation. Across domains, planning and tool-mediated execution have advanced more rapidly than replayability, provenance, robust scientific assessment, external validation, and prospective empirical testing. We therefore argue that agentic bioinformatics should be assessed through workflow correctness rather than final-answer correctness alone. FEV provides a practical basis for comparing systems and designing transparent, auditable, and scientifically accountable bioinformatics workflows.

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Reference graph

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[177]

Genomic objective and planning Define the sequencing, association, diagnostic, perturbation, or gene-editing task
[178]

Analytical role coordination Coordinate data processing, statistical modelling, biological review, and oversight
[179]

Molecular data grounding Resolve samples, assays, genome builds, phenotypes, cohorts, batches, and perturbation conditions Workflow formulation
[180]

Genomic interpretation Ground genes, variants, pathways, diagnoses, and perturbation effects in traceable evidence
[181]

Statistical and causal analysis Evaluate differential expression, gene–trait associations, survival, regulation, and perturbation response
[182]

Genomics tool execution Run alignment, quantification, variant annotation, enrichment, database queries, and guide design Workflow operation Shared genomic evidence and artifacts:FASTQ/BAM files, count matrices, SRA/GEO records, VCF/WES files, HPO and cohort metadata, database...
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Genomic trace capture:record accessions, sample identities, genome builds, database and software versions, parameters, code, intermediate artifacts, failures, repairs, and output lineage
[184]

Genomic verification and escalation:check sample grouping, batch effects, QC, model assumptions, identifiers, evidence support, and uncertainty; revise or escalate unsupported claims Cross- cutting across requirements 1–6
[185]

Optional perturbation or editing validation Test prioritized genes, perturbations, guide RNAs, diagnostic claims, or editing protocols. Closed-loop refinement requires the result to guide a subsequent action Optional empirical extension Figure S6 Cross-cutting workflow bluepri...

2026
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Dataset and context grounding Resolve assays, samples, tissues, species, conditions, cohorts, metadata, and analytical goals
[187]

Cell and spatial representation Represent expression states, markers, clusters, coordinates, neighborhoods, and tissue domains
[188]

Single-cell and spatial tool execution Run preprocessing, integration, clustering, annotation, trajectory, interaction, and spatial analysis Data grounding and execution
[189]

Biological evidence grounding Link annotations, trajectories, interactions, and spatial patterns to traceable biological sources
[190]

Annotation and uncertainty assessment Check marker support, confidence, heterogeneity, spatial coherence, consensus, and expert review Interpretation and assessment Shared single-cell and spatial evidence and artifacts:AnnData and Seurat objects, expression matrices, marker ta...
[191]

Workflow state, critique, and repair:preserve analytical state, inspect failed calls and implausible outputs, revise plans, repair code, rerun affected steps, and record workflow history
[192]

Provenance-preserving reporting:retain dataset identifiers, parameters, code, notebooks, intermediate artifacts, spatial maps, evidence links, uncertainty, review records, and limitations Cross- cutting across requirements 1–5
[193]

Optional prospective biological validation Test an agent-generated annotation, trajectory, interaction, perturbation response, spatial pattern, or biological hypothesis. Closed- loop refinement requires the result to change a subsequent action Optional empirical extension Figu...

2026
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Protein objective and constraints Define the proteomics, structure, binding, function, stability, or sequence-design task
[195]

Protein and proteomics data grounding Resolve sequences, structures, domains, complexes, measurements, templates, and candidate designs
[196]

Molecular tool execution Run structure prediction, docking, sequence design, simulation, scoring, and visualization Workflow formulation and execution
[197]

Biological and mechanistic grounding Link designs and proteomics results to functions, pathways, conserved sites, mechanisms, and literature
[198]

Structural and functional evaluation Assess structures, interfaces, dynamics, stability, binding, function, and developability Candidate evaluation and interpretation Shared protein evidence and artifacts:FASTA sequences, UniProt and PDB records, proteomics measurements, predi...
[199]

Design critique, filtering, and repair:inspect sequence and structure quality, conserved residues, docking or energy scores, failed tools, functional constraints, developability liabilities, and revise weak candidates or workflows
[200]

Trace and provenance preservation:retain identifiers, design constraints, tool versions, parameters, code, intermediate structures, simulation files, design histories, candidate rankings, expert-review records, and limitations Cross- cutting across requirements 1–5
[201]

Optional prospective functional validation Test an agent-generated protein, peptide, binder, nanobody, or enzyme using expression, structural, binding, stability, or functional assays. Closed- loop refinement requires the result to change a subsequent design action Optional em...

2026
[202]

Therapeutic objective and planning Define the target-assessment, molecular-design, safety, treatment, or trial-analysis task
[203]

Therapeutic entity grounding Resolve drugs, SMILES, targets, pathways, patients, cohorts, trials, endpoints, and safety events
[204]

Tool, database, and RAG execution Query biomedical databases, knowledge graphs, molecular tools, safety resources, and trial records Workflow formulation and grounding
[205]

Safety and trial analysis Evaluate adverse events, contraindications, cohorts, endpoints, causal assumptions, and statistical plans
[206]

Mechanistic therapeutic reasoning Link predictions to pathways, drug–target relations, causal evidence, and literature
[207]

Prediction, screening, and molecular design Predict or optimize efficacy, binding, ADMET, toxicity, molecules, and trial outcomes Prediction, reasoning, and safety Shared therapeutic evidence and artifacts:SMILES strings, targets, molecular structures, cohort and trial metadat...
[208]

Critique, verification, and provenance:preserve workflow state and traces; inspect unsupported mechanisms, invalid molecules, safety risks, failed code, causal assumptions, uncertainty, and expert-review records Cross-cutting requirements 1–6
[209]

Optional prospective therapeutic validation Test an agent-generated molecule, target, mechanism, safety prediction, treatment recommendation, or trial decision using aligned experimental, preclinical, or clinical evidence. Closed- loop refinement requires the result to guide a...

2026
[210]

Image objective and context grounding Resolve modality, tissue, magnification, spatial assay, cohort, acquisition, and objective
[211]

Region, cell, and spatial representation Represent regions, cells, tissue compartments, morphology, spatial neighborhoods, and scale
[212]

Image-analysis tool execution Run registration, segmentation, quantification, navigation, classification, and spatial analysis Image grounding and execution
[213]

Biological and spatial grounding Link image findings to molecular measurements, tissue architecture, pathways, biomarkers, and literature
[214]

Quality control and uncertainty Check registration, segmentation, region selection, magnification, confidence, and missing evidence Quality control and interpretation Shared imaging evidence and artifacts:WSIs, microscopy and spatial-omics data, RoIs, image tiles, cell and tis...
[215]

State, navigation, and repair:preserve selected regions, magnification paths, workflow state, failed calls, corrected code, revised parameters, and expert feedback
[216]

Provenance-preserving reporting:retain source images, RoIs, overlays, navigation traces, parameters, analytical outputs, molecular evidence, uncertainty, review records, and limitations Cross-cutting controls 6–7
[217]

Optional prospective biological or clinical validation Test an agent-generated diagnosis, biomarker, spatial interpretation, prognostic claim, or imaging-derived hypothesis using aligned biological, experimental, or clinical evidence. Closed-loop refinement requires the result...

2026
[218]

Research objective and planning Translate the biological goal into data requirements, analytical steps, outputs, constraints, and decisions
[219]

Data and task routing Identify the biological modality and assign suitable tools, databases, models, APIs, or specialist agents
[220]

Evidence-grounded workflow specification Ground analytical choices in literature, documentation, protocols, databases, and workflow examples Workflow formulation and grounding
[221]

Executable code and pipeline generation Generate and run scripts, notebooks, commands, or pipeline stages while managing dependencies and artifacts Executable workflow generation Shared workflow evidence and artifacts:research goals, dataset descriptions, modality metadata, re...
[222]

Workflow state, critique, and repair:maintain analytical state; inspect failed calls and weak intermediate results; revise plans, repair code, update memory, and rerun affected steps
[223]

Verification and provenance preservation:retain dataset identifiers, evidence links, software versions, parameters, code, intermediate artifacts, verification outputs, uncertainty, expert-review records, and limitations
[224]

Cross-domain robustness and transfer:evaluate workflow control across biological modalities, tools, datasets, unseen tasks, prompt variations, and external benchmarks Cross-cutting controls 5–7
[225]

Optional prospective experimental validation Test an agent-generated hypothesis, candidate, perturbation, protocol, or intervention using evidence aligned with the principal claim. Closed-loop refinement requires the result to guide the next action Optional prospective validat...

2026

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

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Autonomous biomedical research with an artificial intelligence agent.Science, page eadz4351, 2026

Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Ryan Li, Yusuf Roohani, Lin Qiu, Shiyi Cao, Gavin Li, et al. Autonomous biomedical research with an artificial intelligence agent.Science, page eadz4351, 2026

2026

[2] [1]

The galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update.Nucleic acids research, 52(W1):W83–W94, 2024

2024

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Uniprot: the universal protein knowledgebase in 2025.Nucleic acids research, 53(D1):D609–D617, 2025

2025

[4] [3]

The gene ontology knowledgebase in 2026.Nucleic Acids Research, 54(D1):D1779–D1792, 2026

2026

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Llm4grn: Discovering causal gene regulatory networks with llms– evaluation through synthetic data generation

Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte, Waqar Hanif, Shubhi Ambast, Charles Mwangi Kaumbutha, Matthias Becker, and Mario Fritz. Llm4grn: Discovering causal gene regulatory networks with llms– evaluation through synthetic data generation. InICLR 2025 Workshop on Machine Learning for Genomics Explorations

2025

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Multi-agent ai enables evidence-based cell annotation in single-cell transcriptomics

Gautam Ahuja, Alex Antill, Yi Su, Giovanni Marco Dall ˘2019Olio, Sukhitha Basnayake, Göran Karlsson, and Parashar Dhapola. Multi-agent ai enables evidence-based cell annotation in single-cell transcriptomics. bioRxiv, pages 2025–11, 2025

2025

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Cellvoyager: Ai compbio agent generates new insights by autonomously analyzing biological data.Nature Methods, 23(4):749–759, 2026

Samuel Alber, Bowen Chen, Eric Sun, Alina Isakova, Aaron J Wilk, and James Zou. Cellvoyager: Ai compbio agent generates new insights by autonomously analyzing biological data.Nature Methods, 23(4):749–759, 2026

2026

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Tactic: An explainable multi-agent architecture for classification & interpretable reasoning in spatial transcriptomics

Abdel Rahman Alsabbagh, Mahmoud Zahran, Ali Balubaid, Sumeer Ahmad Khan, Robert Lehmann, Xabier Martinez de Morentin, Vincenzo Lagani, Narsis A Kiani, David Gomez-Cabrero, and Jesper Tegnér. Tactic: An explainable multi-agent architecture for classification & interpretable reasoning in spatial transcriptomics. InICML 2025 Generative AI and Biology (GenBio...

2025