Common Sense Analytics

Biology + AI + Validation

Beyond prediction.
Evidence for discovery.

Common Sense Analytics is an AI-native CRO developing context-aware evidence technology for biological discovery, beginning with protein-interaction evidence synthesis.

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We work with: Academic laboratories Core facilities Biotech & pharma R&D
Biological Discovery
Hypothesis
Model
Interpretation
Validation

A complete research perspective

AI predictions are a starting point,
not a scientific conclusion.

Rigorous discovery requires an understanding of what a model has learned, why the result matters biologically, and where credible validation can come from.

Discover

Apply AI and bioinformatics to reveal patterns, candidate drivers, biomarkers, targets, and testable hypotheses.

Interpret

Resolve ambiguous signals into scientific structure, identify confounding, and translate model behavior into biological meaning.

Validate

Test the emerging structure in silico and at the bench, establishing a robust building block within the wider body of scientific knowledge.

The research workflow

From question to
defensible evidence.

This iterative process connects computational rigor with biological insight. At every stage, results can refine the model, the validation plan, or the original research question.

01

Define

Frame the biological question and establish the standard of evidence.

Question
02

Design

Select data, models, controls, baselines, and validation criteria.

Strategy
03

Discover

Build analyses that uncover relationships and generate hypotheses.

Model
04

Interpret

Examine biological drivers, stability, plausibility, and confounding.

Meaning
05

Validate in silico

Use independent data, orthogonal methods, perturbations, and controls.

Evidence
06

Validate at the bench

Translate findings into experiments with clear endpoints and controls.

Experiment
07

Communicate

Deliver reproducible methods, figures, and publication-ready evidence.

Impact

Validation is not the end of a pipeline. It is part of a research cycle that strengthens the question, the model, and the resulting conclusions.

Research capabilities

Focused expertise for ambitious biological questions.

Engagements are shaped around the science, from a focused interpretability analysis to an end-to-end collaborative research program.

Interpretable & explainable AI

Model understanding, feature attribution, stability, bias, and confounding.

Custom machine learning

Predictive modeling, biomarker discovery, benchmarking, and method development.

Bioinformatics & data integration

Omics, imaging, multimodal data, public datasets, and reproducible pipelines.

Biological validation strategy

In silico evidence, experimental design, controls, endpoints, and collaborator support.

Scientific communication

Publication-ready analyses, figures, methods, grants, and confidential R&D reports.

Ways to collaborate

Shaped around your scientific objective.

End-to-end discovery

A complete collaborative study, from research question and model design to validation and manuscript-ready outputs.

Specialist contribution

Interpretability, validation, or AI method development within a broader academic or industry research program.

Research de-risking

Feasibility studies, public-data analyses, benchmarking, and preliminary evidence for grants or R&D decisions.

Engagements can support open academic publication or confidential industry research, with deliverables adapted to each collaboration.

What we are building

Infrastructure for the decisions between prediction, evidence, and discovery.

Common Sense Analytics is an AI-native CRO developing context-aware evidence technology for the decisions between prediction and experimental validation. Paid research engagements provide the deployment and learning environment for workflows that can be standardized in software.

Scientific uncertainty cannot be eliminated. Research waste can be minimized.

Explore

Context-aware evidence engine for biological discovery.

Our first product direction is a protein-interaction evidence engine integrating proteomics and other experimental data, structure-based predictions, scientific literature, and experimental context. It ranks candidates, evaluates and explains the supporting evidence, and recommends the next in silico or experimental validation step. The same evidence architecture can extend to biomarkers, therapeutic targets, and other biological hypotheses.

Literature Experiments Biological predictions WWW Biostatistics

Integrate

Interpret

Prioritize

Established 2019

Based in New York City

Experience across research environments

  • Biotechnology
  • Universities
  • Hospitals

About Common Sense Analytics

Established expertise across computation and experiment.

Founded in 2019 by Niki Athanasiadou, PhD.

Niki has worked with biotechnology companies, university research groups, and hospitals, bringing scientific and computational perspectives together in rigorous AI-mediated biological research.

LinkedIn Research

Start a conversation

Are you ready to apply AI to your biological research?

Whether you have a defined research question or are beginning to explore what AI could offer, a thoughtful conversation is a good place to start.

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