Welcome to BioLayers AI.

We are building an AI platform that transforms biomedical literature into living knowledge.

Instead of reading hundreds of papers manually, researchers can explore connected evidence, discover biological relationships, and generate new hypotheses through an interactive knowledge graph.

Current Stage

• Functional MVP
• Knowledge Graph Engine
• Evidence Explorer
• AI-Assisted Discovery

Our Vision

Transform millions of scientific publications into one connected, searchable, and explainable knowledge network that accelerates biomedical discovery.

Website: Coming Soon
GitHub: Coming Soon

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From Papers to Living Maps.
@biolayersai

The Literature Problem

How many scientific papers are published every day?

Around 10,000+ new biomedical papers appear every single day.

No researcher can read them all. Yet every paper may contain a small piece of evidence that could change the direction of future discoveries. The challenge isn't the lack of knowledge.

It's that knowledge is fragmented across millions of publications.

Researchers spend countless hours:

• Searching for relevant papers
• Comparing conflicting findings
• Connecting evidence manually
• Missing important relationships hidden in the literature

What if scientific knowledge could be explored as a network instead of thousands of PDFs?

Imagine asking:

"Show every known relationship between CAFs and prostate cancer metastasis."

Instead of reading hundreds of papers, you immediately explore connected evidence. This is the future we're building at BioLayers AI. From scattered publications to living knowledge.

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From Papers to Living Maps.
@biolayersai

From Search to Discovery

Today, biomedical research begins with a search bar. Researchers type keywords into databases, open dozens of papers, and manually connect pieces of evidence.

But what if AI could do more than search?

Imagine asking:

"How do cancer-associated fibroblasts contribute to prostate cancer bone metastasis?"

Instead of returning hundreds of papers... BioLayers AI builds a living knowledge graph.

It automatically identifies:

• Biological entities (genes, proteins, diseases)
• Relationships supported by scientific evidence
• Connections across thousands of publications

Rather than reading papers one by one, you explore an interconnected map of scientific knowledge.
Every node is supported by literature. Every relationship is traceable to its evidence. Every discovery becomes easier to understand.

This isn't just literature search. It's a new way of thinking about biomedical knowledge.

From information to intelligence. From isolated papers

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From Papers to Living Maps.
@biolayersai

Knowledge Isn't Flat. Biology Is a Network.

Most scientific papers tell one story. But biology doesn't work that way. Every gene interacts with proteins. Every protein regulates pathways. Every pathway influences cells. Every cell shapes disease. The challenge isn't reading one paper.

It's understanding how millions of discoveries connect.

Imagine asking:

"How do CAFs influence immune suppression in metastatic prostate cancer?"

Instead of reading hundreds of publications... BioLayers AI builds an interconnected knowledge graph that allows researchers to explore biological relationships across genes, proteins, pathways, cell types, diseases, and therapeutic targets.

Every connection is linked back to scientific evidence. Instead of searching for papers...

You explore the biology itself.

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From Papers to Living Maps.
@biolayersai

🚀 BioLayers AI is now on LinkedIn!

Today I'm excited to officially launch the BioLayers AI LinkedIn page.

BioLayers AI is an AI-driven initiative to transform biomedical literature into living knowledge graphs—making it easier to explore hidden relationships between genes, proteins, cells, pathways, diseases, and therapies.

Instead of reading hundreds of papers one by one, imagine navigating scientific knowledge as an interconnected map.

This is just the beginning.

Over the coming months, I'll be sharing:

🧬 AI for biomedical discovery
📚 Computational oncology insights
🕸 Knowledge graph development
🧠 Research ideas and project updates
🚀 The journey of building BioLayers AI

If you're interested in AI, computational biology, precision medicine, or cancer research, I'd love to connect.

👉 Follow BioLayers AI and join us as we build the future of biomedical discovery.

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From Papers to Living Maps.
@biolayersai

🚀 BioLayers AI — From Papers to Living Maps.

Every biomedical paper contains valuable knowledge, but discovering connections across thousands of studies is difficult.

We're building BioLayers AI — an AI-powered platform that transforms biomedical literature into interactive biological knowledge maps, helping researchers explore relationships between genes, proteins, pathways, diseases, and therapies.

🧬 Watch our first demo below.

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From Papers to Living Maps.
@biolayersai

🧬 BioLayers AI Update — From Connections to Evidence.

We’ve now integrated PubMed literature analysis directly into BioLayers AI.

For any relationship in the knowledge graph, BioLayers can:

🔎 Search PubMed for relevant publications
📄 Retrieve paper abstracts and metadata
✅ Identify supporting evidence
⚠️ Detect contradicting evidence
🔗 Separate contextual from unrelated studies
📊 Estimate the overall strength of literature support

For example:

Cancer-associated fibroblasts → secretes → CXCL12

BioLayers AI retrieved 244 candidate publications, loaded the most relevant papers and analyzed their abstracts to determine how strongly the literature supports this specific biological relationship.

The goal is simple:

Don’t just show biological connections. Show the evidence behind them.

This is another step toward turning BioLayers AI from a knowledge-graph visualizer into a real computational oncology research workspace.

More coming soon.
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From Papers to Living Maps.
@biolayersai

🧬 Give BioLayers AI a biological relationship to investigate.

Let’s try something different. Instead of showing you what we built, this time you choose what BioLayers investigates.

Send any cancer-related biological mechanism in this format:

Cell / Gene / Protein → Relationship → Target

For example:

Cancer-associated fibroblasts → secrete → CXCL12

BioLayers AI will then:

🔎 Search PubMed for relevant publications
✅ Identify supporting evidence
⚠️ Detect contradictory findings
🧬 Build the biological mechanism into an explorable map

I’ll choose one of your suggestions and publish the complete analysis here — including the papers, evidence classification, contradictions, and final mechanism.

What should BioLayers investigate next?

🧬 TP53 → regulates → apoptosis
🧬 TGF-β → activates → cancer-associated fibroblasts
🧬 CXCL12 → promotes → metastasis

💬 Suggest your own mechanism; DM me @gutsy_warrior

Let’s see where the evidence leads.

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From Papers to Living Maps.
@biolayersai

Can two scientific papers both be correct when they disagree?

Sounds like a contradiction. In biomedical research, it happens constantly.

One paper says a pathway suppresses tumor growth. Another says it drives it.

Neither is wrong. They're just answering in a different context — different cell type, different model, different disease stage.

This is called conflicting evidence, and most literature reviews flatten it into a simple yes or no, when the real answer is "it depends."
This is exactly what we're working on with BioLayers AI.

It doesn't just count papers that agree. It actively looks for papers that disagree — and tries to show why.

🔎 Finds both supporting and contradicting studies
⚠️ Flags real conflicts in the evidence
🔗 Traces the disagreement back to context

Because sometimes the most useful thing a knowledge graph can say isn't "this is true." It's "here's where the literature disagrees — and why."

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From Papers to Living Maps.
@biolayersai

What if the missing evidence is more important than the existing evidence?

Scientists usually search for confirmation. But the most interesting question isn't "what proves this?" — it's "what's missing?"

We know A affects B.
We know B affects C.
But nobody has clearly established A → C.

Most literature reviews stop at what's documented. They don't flag what's absent.
Yet an unestablished link isn't nothing — it's often exactly where the next hypothesis begins.

This is what BioLayers AI is built to surface:
🔎 Maps existing, evidence-backed connections
🕳 Highlights gaps between known relationships
💡 Turns missing links into testable hypotheses

Because a knowledge graph shouldn't just show what science has proven. It should show where science hasn't looked yet.
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From Papers to Living Maps.
@biolayersai