About LYFE Sciences

Built by a Genome Analyst,
for Everyone

LYFE Sciences
matthewlyf
Project HERA

01 — Origin

How It Started

LYFE Sciences began as a personal project in 2023 to simplify genetic variant interpretation in day-to-day work. As a genome analyst, I frequently encountered the challenge of quickly accessing comprehensive and reliable genetic variant information.

To address this need, I built LYFE Sciences. an accessible, user-friendly tool that consolidates crucial genetic data in one place.

02 — Mission

What I'm Trying to Do

My main goal is to streamline variant analysis by reducing the need to navigate between multiple resources or pay for third-party applications.

I continually update LYFE Sciences with the latest AI architectures and enhancements to keep it practical, relevant, and useful for everyday clinical and research work.

03 — Approach

How It Works

LYFE Sciences is designed around the real workflows of genomic professionals. Rather than requiring users to juggle ClinVar, OncoKB, JAX-CKB, COSMIC, and other databases independently, Project HERA aggregates, normalises, and presents that data in a unified interface.

Every query runs against up-to-date sources so that interpretations reflect the latest evidence and not a static snapshot from months ago.

The tool is continuously refined based on real-world usage and the evolving landscape of genomic databases.
Try a variant query
04 — Project HERA

The Next Step: Project HERA

Project HERA is my third iteration of the LYFE SCIENCES project and is a modular AI pipeline for variant interpretation that integrates retrieval, normalization, framework selection, criterion assessment, and structured result generation into a single workflow.

It is designed around the idea that reliable AI needs controlled inputs, explicit evidence mapping, framework-aware decision logic, and outputs that are easy to audit and operationalize.

The pipeline combines automated evidence gathering with agentic-based decision making, prioritizes ClinGen and gene-specific specifications, and produces structured review artifacts for downstream systems.

Retrieval
Normalization
Framework Selection
Criterion Assessment
Structured Output

Project HERA is an example of how AI-native pipelines can bridge language models, domain-specific logic, and production software architecture, without sacrificing the traceability that clinical use demands.

05 — Impact

Why It Matters

By sharing LYFE Sciences, I hope to make genetic analysis less overwhelming; helping others like myself improve their work.

Faster variant interpretation means analysts spend less time hunting for information!

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