Research at the intersection of biology and artificial intelligence.
AlynaMOM focuses on the potential of AI-assisted scientific research to support the investigation of breast cancer biology.
Our approach is centered on helping researchers work with complex biomedical information, explore relationships across scientific evidence, and formulate questions for further investigation.
Scientific Literature Analysis
Biomedical research produces tens of thousands of peer-reviewed articles each year. In breast cancer oncology alone, researchers must navigate conflicting reports, distinct histological cohorts, and varied experimental setups across in vitro cell lines and clinical trials.
AlynaMOM is exploring how advanced language models can support researchers by assisting with:
Scientific Paper Discovery & Organization
Assisting researchers in navigating large corpora of published studies according to molecular phenotypes, genetic drivers, and experimental conditions.
Literature Summarization
Synthesizing multi-study methodologies, study cohorts, and key experimental findings into structured, readable research briefings.
Comparison of Published Findings
Systematically highlighting areas of consensus and divergent outcomes across independent oncology papers.
Extraction of Relevant Research Concepts
Isolating specific biochemical pathways, receptor dynamics, resistance mechanisms, and microenvironment variables.
Identification of Open Research Questions
Highlighting persistent ambiguities, untracked secondary endpoints, or unverified mechanistic assumptions in literature.
Organization of References & Evidence
Attributing every synthesized observation to its primary DOI reference and specific paragraph for fast source validation.
Biological Data and Computational Methods
Breast cancer is not a monolithic pathology. It encompasses extensive heterogeneity across molecular subtypes—such as Luminal A, Luminal B, HER2-enriched, and Triple-Negative Breast Cancer (TNBC)—as well as variable immune infiltration and stromal interactions.
We are actively investigating the computational foundations needed to process complex biological information:
Neural Network Architectures & Semantic Graph Embeddings
Evaluating transformer-based representations and graph embeddings capable of mapping relationships between genes, metabolic pathways, and pharmacological compounds without losing contextual semantics.
Biomedical Information Processing
Structuring unstructured experimental protocols, supplementary data tables, and biomarker registries into standardized ontologies.
Pattern Exploration & Biological Variables
Analyzing how multi-variable correlations (e.g., hormone receptor expression, mutational burden, and patient demographic indicators) are discussed in biomedical cohorts.
Computational Hypothesis Generation
One of the most promising frontiers of AI in biology is its potential to assist researchers in formulating testable scientific questions by connecting findings across disciplines that rarely interact directly.
From Observation to Experimental Inquiry
For instance, an observation regarding nutrient scavenging in a non-oncology cell biology paper might suggest an unexamined pathway in triple-negative breast cancer metabolic adaptation. AI models can help surface these cross-domain adjacencies.
Research Integrity & Epistemic Standards
Evidence-Based Reasoning
Every assertion produced by the platform must be directly traceable to peer-reviewed literature or verified biological datasets.
Source Traceability
Direct linking to original DOIs, PMIDs, journal volumes, and author contributions ensures researchers can inspect primary context immediately.
Scientific Reproducibility
Prompt architectures, model hyperparameters, and retrieval filters are versioned to support reproducible computational inquiry.
Transparent Limitations
The platform explicitly displays its uncertainty boundaries and potential sampling biases without false precision.
Human Expert Oversight
Qualified researchers maintain total intellectual ownership and oversight; computational outputs are auxiliary tools.
Responsible Data Governance
Commitment to strict privacy standards, ethical data sourcing, and refusal to ingest or process unconsented patient health information.
Building with scientific rigor.
AlynaMOM is currently in an early development and internal testing phase. We are evaluating potential AI-assisted research workflows and defining the technical foundations of our platform.
Our public website introduces our mission and research direction. The platform itself is not yet publicly available.