BioML-Bench: Benchmarking ML in Life Sciences

• BioML-Bench is a set of systematic benchmarks that evaluate ML and foundation models across diverse biological research domains.
• It standardizes evaluations on tasks in genomics, lab automation, biomedical signals, scientific vision, and multi-omics reasoning using reproducible methodologies.
• It leverages modular architectures and standardized metrics, enabling robust algorithm comparisons and advancing bioinformatics research.

BioML-Bench refers to the emerging class of systematic benchmarks designed for evaluating ML and foundation models in biology and the life sciences, including genomics, biomedical signal processing, vision for scientific domains, laboratory workflows, and multi-omics reasoning. These platforms, unified under the "BioML-Bench" concept, deliver reproducible, domain-grounded, and extensible frameworks that mirror real-world bioinformatics and biological research tasks. They serve as reference points for progress in algorithmic capabilities, dataset curation, and evaluation practices across subfields such as genomics, clinical wearables, laboratory automation, and mechanistic biology.

1. Purpose and Scope

BioML-Bench platforms were developed in response to the limitations of prior benchmarks that were either domain-agnostic, relied on textbook knowledge, or failed to capture the operational requirements of modern biological research. These benchmarks address the need for:

• Rigorous, standardized evaluation of ML and LLM systems on tasks critical for bioinformatics, laboratory workflows, and biomedical signal monitoring
• Coverage of both discriminative and generative biological tasks, spanning short-range motif recognition, multi-modal figure and table reasoning, long-range genomics, wearable signal processing, and knowledge-based mechanism elucidation
• Reproducibility and community extensibility, fostering the development of robust, generalizable, and interpretable algorithms.

BioML-Bench frameworks include, among others, LAB-Bench (laboratory task benchmarking) (Laurent et al., 2024), OmniGenBench (genomic foundation model evaluation) (Yang et al., 20 May 2025), GenBench (systematic genomics assessment) (Liu et al., 2024), BiomedBench (hardware-aware TinyML for wearables) (Samakovlis et al., 2024), BioBench (scientific vision in ecology) (Stevens, 20 Nov 2025), and BIOME-Bench (multi-omics pathway mechanism inference) (Wei et al., 31 Dec 2025).

2. Core Task Taxonomy and Biological Workflows

BioML-Bench implementations span a range of biological applications, each reflecting operational bottlenecks or high-value decision points:

• Genomics: Sequence classification (coding/noncoding region labeling), motif discovery, splicing, enhancer/promoter inference, 3D chromatin structure regression, gene expression prediction, variant impact analysis. GenBench and OmniGenBench explicitly divide tasks into short-range (≤1 kb) and long-range (up to 256 Mb) regimes with architectural implications (Liu et al., 2024, Yang et al., 20 May 2025).
• Lab-scale Tasks: Literature search and reasoning, figure/table interpretation, database navigation (e.g., ClinVar vs. OMIM gene queries), sequence manipulation (primer design, restriction mapping, ORF finding, GC-content), experimental protocol troubleshooting, and "human-hard" molecular cloning scenarios (Laurent et al., 2024). These tasks reflect daily workflows in experimental biology.
• Biomedical Sensor Pipelines: End-to-end signal acquisition and processing for TinyML platforms, including cardiac (ECG), neurological (EEG), muscular (sEMG), respiratory, and multimodal sensor tasks in wearables, evaluated for both energy and inference accuracy on diverse hardware (Samakovlis et al., 2024).
• Scientific Computer Vision: Ecological and scientific imaging tasks—taxonomy classification, functional-trait inference, behavior recognition across plant, animal, fungal, and microbial domains—with acquisition modality diversity (RGB, micrograph, IR, video frame), extending beyond the ImageNet paradigm (Stevens, 20 Nov 2025).
• Multi-Omics Pathway Reasoning: Fine-grained biomolecular interaction inference (e.g., regulatory, post-translational modification, metabolic, and causal phenotype effects) and end-to-end multi-omics pathway mechanism generation directly from literature, operationalized as structured relation extraction and explanation tasks (Wei et al., 31 Dec 2025).

3. Dataset Construction and Annotation Practices

BioML-Bench datasets are characterized by a mix of automated code-driven generation, expert manual curation, and multi-faceted annotation pipelines:

• Manual Drafting and Calibration: For high-complexity or "human-hard" tasks (e.g., CloningScenarios, FigQA), PhD-level domain experts draft, iteratively refine, and calibrate instances, ensuring both biological plausibility and distractor quality (Laurent et al., 2024).
• Programmatic Expansion: Large-scale tasks (e.g., sequence-based subtasks, database queries) are synthesized programmatically using public data sources (E. coli, Human/Mouse/Plant/Yeast genome/annotation resources, ClinVar, Ensembl, PubChemPy, MyGene.info, UniProt), with template-driven distractor and augmentation logic (Laurent et al., 2024, Yang et al., 20 May 2025).
• Public/Private Splitting: A minimum 20% holdout is standard, enforcing a separation between public (community use/leaderboard) and private (unseen) splits, with negligible observed leakage or performance difference (<5% absolute accuracy gap) (Laurent et al., 2024).
• Knowledge Graph Structuring: In multi-omics pathway settings, raw LLM-extracted entities are normalized and structured into core interaction "hexaplets" (source entity, state, relation, target, state, context), with gold-standard validation and natural-language mechanistic references (Wei et al., 31 Dec 2025).
• Task Diversity and Biological Breadth: Datasets jointly span molecular biology, genomics, physiology, ecology, and mechanistic inference to ensure representativeness.

4. Evaluation Protocols and Metrics

A multi-metric, reproducibility-focused approach is standard:

• Accuracy, Precision, Coverage: For laboratory and knowledge tasks, definitions align as follows (using LaTeX):
  • $$\mathrm{Accuracy} = \frac{\text{Number of Correct Answers}}{\text{Total Number of Questions}}$$
  • $$\mathrm{Precision} = \frac{\text{Correct}}{\text{Attempted (not “Insufficient information”)}}$$
  • $$\mathrm{Coverage} = \frac{\text{Attempted}}{\text{Total}}$$ (Laurent et al., 2024)
• Task-Specific Metrics: F1 score, AUROC, Spearman/Pearson correlation, RMSE, and cosine similarity are central for sequence and regression tasks; macro-F1 for long-tailed vision tasks; micro-average for multi-label settings (FishNet); top-k accuracy for granular species-classification (Yang et al., 20 May 2025, Liu et al., 2024, Stevens, 20 Nov 2025).
• Structured and Semantic Evaluations: Pathway mechanism generation is scored by LLM-judge (phenotype coverage, causal reasoning, factuality, hallucination), graph coverage ($$\mathrm{Coverage} = |\mathcal{T}_{\mathrm{pred}}|/|\mathcal{T}_{\mathrm{GT}}|$$), embedding similarity, and (optionally) explanation fidelity (Wei et al., 31 Dec 2025).
• Human Baselines and Selective Classification: Human experts are directly benchmarked where feasible, using identical metrics/coverage conventions and allowed "unsure" (analogous to "Insufficient information") choices (Laurent et al., 2024).
• Prompting and Automation: Zero-shot, chain-of-thought prompting is standard in LLM benchmarks; model answer extraction uses both regex parsing and fallback LLM-based label extraction for robustness (Laurent et al., 2024).
• Hardware-aware Performance: BiomedBench adds cycle count, energy breakdown (idle/acquisition/processing in mJ), and platform-specific performance for TinyML deployments (Samakovlis et al., 2024).

5. Software Architecture, Modularity, and Reproducibility

BioML-Bench systems are implemented as modular, extensible, and containerized pipelines enabling one-command reproducible evaluation:

• Layered Architecture: Platforms delineate Data (raw biological files, preprocessing, tensorization), Model (unified registry with standardized API and wrappers), Benchmarking (task/metric registries, orchestrated experiment runners), Interpretability (motif/attention/embedding explainers) (Yang et al., 20 May 2025).
• Data Provenance and Traceability: Every file transformation (trimming, filtering, tokenization, normalization) is logged for traceability, supporting both reproducibility and regulatory compliance (Yang et al., 20 May 2025).
• Unified APIs and CLIs: Standardized interfaces exposed in Python, YAML/JSON configs, and command-line utilities (bench run, bench explain) facilitate reproducible runs and integration into CI/CD workflows with Docker/Singularity orchestration (Yang et al., 20 May 2025).
• Extensibility via Plugins: New data types, models, benchmarks, or explainers are registered via plugin architecture (entry points, minimal boilerplate), lowering the barrier for community-driven expansion, and enabling leaderboard/ecosystem effects (Yang et al., 20 May 2025, Liu et al., 2024).
• Downstream API Simplification: For vision benchmarks (BioBench), a uniform embedding API isolates backbone quality from task-specific heads or tuning, with probing and metric computation streamlined via a single Python module (Stevens, 20 Nov 2025).

6. Model Performance and Key Insights

Multi-domain benchmarking has revealed strengths, weaknesses, and emerging trends across biological ML models:

• Laboratory and Molecular Tasks: State-of-the-art LLMs lag expert human precision in complex figure interpretation, literature reasoning, protocol troubleshooting, and especially "human-hard" molecular cloning scenarios, with open-response performance dropping further due to the elimination of weak distractors (Laurent et al., 2024).
• Genomic Models: Large attention-based GFMs (e.g., Nucleotide Transformer, GENA-LM) dominate short-range sequence tasks, but convolutional/state-space models (HyenaDNA, Caduceus) scale better for long-range and 3D structure contexts (Liu et al., 2024). Diminishing returns are observed above ~100M parameters for certain local motifs.
• Wearable Biomedical Applications: No single hardware platform matches all TinyML application regimes; energy/performance tradeoffs are dominated by workload characteristics (float/integer ratio, duty cycle, acquisition bandwidth) and MCU-specific features (deep-sleep current, FPU/vector MACs, cluster size) (Samakovlis et al., 2024).
• Ecological Vision: ImageNet-1K accuracy is not predictive of scientific vision performance at state-of-the-art levels; mis-ranking rates (ImageNet vs. domain-relevant macro-F1) reach 22–30% for high-accuracy models, emphasizing the necessity of science-specific evaluation (Stevens, 20 Nov 2025).
• Pathway Mechanism Elucidation: LLMs reach baseline factuality in multi-omics mechanism summarization, but remain limited in fine-grained interaction discrimination and phenotype-level explanation coverage; judge metrics, coverage, and embedding similarity can diverge, underlining the complexity of and challenges in holistic scientific reasoning (Wei et al., 31 Dec 2025).

7. Best Practices, Limitations, and Future Directions

Consensus best practices and recognized limitations inform the ongoing evolution of BioML-Bench efforts:

• Best Practices: Multi-modal, multi-scale task inclusion (text, sequence, figure, signal, table, graph); hybrid data generation (manual + code); open public/private splits; validation of distractors; inclusion of open-response and tool-augmented evaluation settings; multi-metric reporting (accuracy, precision, recall, latency, memory, power, explainability); full code/data/protocol transparency (Laurent et al., 2024, Yang et al., 20 May 2025, Liu et al., 2024).
• Limitations: Human-hard and open-response benchmarks are annotation-expensive; existing models exploit weak distractors and suffer in free-form settings; evaluation without plugin tool augmentation (e.g., BLAST, RAG, bioinformatics APIs) underestimates attainable performance; prompt and test structure sensitivity is not systematically characterized; multi-pathway, cross-document inference remains a challenge (Laurent et al., 2024, Wei et al., 31 Dec 2025).
• Future Recommendations: Extend to broader subdomains—transcriptome/proteome, structure/docking, CRISPR design, chemical biology, cross-task mechanistic graphs; standardize metrics, documentation, and reproducibility norms; expand to generative assessment (DNA design, variant effect, synthesis); integrate tool-augmented agents; diversify task phrasing and format; provide "proof-of-possibility" evaluations for annotation-limited domains (Laurent et al., 2024, Yang et al., 20 May 2025, Wei et al., 31 Dec 2025).

By synthesizing modularity, reproducibility, ecological validity, and extensibility across diverse biological research workflows, BioML-Bench comprises the reference framework for systematic, scalable, and biologically meaningful evaluation of current and future machine learning capabilities in the life sciences.