Thirty Public-Safe Life-Sciences Prompts
Add your research summary, assay design, biomarker panel, biological model,
dataset, device concept, clinical workflow, or validation plan. These prompts
prepare a structured comparison but do not reveal the private Ontomics framework.
Coming soon framework preparation
Disease-Agnostic Detection and Cross-Disease Comparison
Prepare a research program for later comparison with the public mitochondrial disease-agnostic detection framework without exposing controlled mechanisms or patent material.
1
Prepare this life-science research program for comparison with a mitochondrial disease-agnostic detection framework. Map the proposed upstream signals, downstream disease markers, timing assumptions, and evidence needed for a serious comparison.
2
Identify which biological changes in this dataset may be shared across multiple diseases and which appear condition-specific. Separate common cellular stress patterns from diagnosis-dependent outcomes.
3
Design an intellectual-property-safe pilot that tests whether a common mitochondrial or bioenergetic signal can be compared across conditions without disclosing private Ontomics methods.
Molecular, cell, and developmental biology
Molecular Biology, Cell Biology, and Developmental Biology
Use these prompts to connect molecules, organelles, cells, tissues, development, and whole-system behavior rather than treating each biological layer independently.
4
Review this molecular biology model and identify where gene, protein, metabolite, organelle, and cell-state explanations fail to connect into one causal sequence.
5
Evaluate this cell biology experiment for timing, compartment effects, cellular heterogeneity, stress adaptation, and whether the measured endpoint is upstream or downstream of the proposed mechanism.
6
Compare this developmental biology model across molecular, cellular, tissue, and organism scales. Identify the missing mechanical, energetic, or signaling transitions between stages.
Genetics and regulation
Genetics, Genomics, Epigenetics, and Gene Regulation
Examine whether genomic associations are being mistaken for complete mechanisms and where cellular state, environment, structure, and timing must be added.
7
Review this genetics or genomics study and separate association, causal evidence, regulatory interpretation, environmental dependence, and downstream phenotype.
8
Evaluate whether this epigenetic signal is a driver, a compensatory response, or a record of an earlier cellular-state change. Identify the experiments needed to distinguish those possibilities.
9
Prepare this genomic framework for disease-agnostic comparison. Identify which features could represent common upstream stress and which are likely disease-, tissue-, or cohort-specific.
Systems and computation
Systems Biology, Bioinformatics, and Computational Biology
Use these prompts to test whether network models, omics pipelines, and biological predictions have enough mechanistic closure to survive new data.
10
Audit this systems-biology model for missing variables, assumed edges, feedback loops, scale mismatch, parameter identifiability, and whether the network remains valid when new data arrives.
11
Review this bioinformatics or computational-biology pipeline for cohort bias, batch effects, feature leakage, circular validation, biological plausibility, and reproducibility.
12
Design a cross-disease computational comparison using mitochondrial, metabolic, transcriptomic, proteomic, or clinical data while preserving disease-specific context and avoiding overgeneralization.
Metabolism and cellular energy
Biochemistry, Metabolism, Bioenergetics, and Mitochondrial Science
Examine energy use, metabolic adaptation, cellular stress, organelle communication, and the timing of state changes without publishing private framework variables.
13
Review this metabolic or bioenergetic model and identify where energy production, substrate use, redox balance, signaling, and cellular-state change are causally connected or merely correlated.
14
Evaluate whether this mitochondrial signal is an early system change, a compensatory response, a late consequence, or a measurement artifact. Define the minimum evidence needed to decide.
15
Compare multiple disease datasets for shared mitochondrial or bioenergetic instability using only public biological measures. Identify a safe pilot that could justify deeper framework access.
Brain and nervous system
Neuroscience, Neurobiology, Neurodevelopment, and Neurodegeneration
Connect cellular energetics, neural activity, development, behavior, and degeneration while maintaining a clear boundary between exploratory research and clinical claims.
16
Review this neuroscience model across mitochondrial function, cellular metabolism, synapses, circuits, behavior, and clinical observations. Identify where the cross-scale explanation breaks.
17
Evaluate this neurodevelopmental or neurodegenerative biomarker strategy for timing, specificity, cohort variability, confounding, and whether it could participate in a disease-agnostic comparison.
18
Design a non-diagnostic research pilot comparing early cellular or bioenergetic signals across neurological conditions without claiming clinical validity.
Immunity and biological interaction
Immunology, Microbiology, Virology, Inflammation, and Host Response
Use these prompts to distinguish pathogen-specific responses from shared host stress, immune metabolism, inflammation, and recovery dynamics.
19
Compare this immunology or inflammation model across immune-cell state, metabolism, signaling, tissue context, timing, and recovery. Identify where cause and consequence are being confused.
20
Review this microbiology or virology study for pathogen effects, host-state effects, cohort differences, and shared cellular stress responses that may generalize beyond one infection.
21
Prepare this host-response dataset for disease-agnostic analysis. Separate universal stress signals, immune-specific mechanisms, pathogen-specific features, and treatment effects.
Diagnostics and precision medicine
Biomarkers, Diagnostics, Precision Medicine, and Clinical Systems
Evaluate whether tests measure early system change, late disease burden, treatment response, or only statistical separation within one dataset.
22
Review this biomarker or diagnostic panel for biological timing, analytical validity, clinical validity, specificity, sensitivity, cohort transfer, and whether it measures upstream or downstream change.
23
Compare this precision-medicine architecture with a disease-agnostic detection strategy. Identify what can be shared across diseases and what must remain condition-, tissue-, and patient-specific.
24
Design a staged validation path from exploratory biological signal to research assay, retrospective study, prospective study, and possible clinical use without skipping evidence levels.
Engineering and measurement
Biomedical Engineering, Biosensors, Bioelectronics, and Digital Health
Use these prompts for devices, assays, sensors, wearable systems, data capture, artificial intelligence, and integration into real biological and clinical workflows.
25
Review this biomedical device or biosensor for biological target selection, signal quality, calibration, drift, sampling context, confounding, and integration with the intended workflow.
26
Evaluate this digital-health or clinical-AI system for data provenance, physiological relevance, missing measurements, alert burden, human oversight, and transfer across patient populations.
27
Define a two-week technical pilot for a disease-agnostic sensing concept using public measures and a narrow research question while excluding private Ontomics mechanisms.
Translation and intervention
Pharmacology, Drug Discovery, Toxicology, Aging, and Regenerative Medicine
Connect target selection, system response, toxicity, adaptation, aging, repair, and therapeutic timing without treating one pathway as the entire organism.
28
Review this drug-discovery program for target validity, pathway redundancy, cellular adaptation, mitochondrial effects, off-target risk, and the gap between model systems and human biology.
29
Evaluate this toxicology or safety dataset for early cellular stress, dose timing, recovery, tissue specificity, and whether shared bioenergetic signals could improve interpretation.
30
Compare this aging or regenerative-medicine framework with a disease-agnostic cellular-state approach. Identify common repair, stress, and energy questions that could support a public-safe pilot.