Memory organized by functional region
Local patterns are modeled in partitioned genomic contexts, reducing semantic mixing and preserving representations that better reflect coding, regulatory, and other functional regions.
Genome Foundation Model
Meno Genome Foundation Model
Understand genomic sequences and turn the rules of life into searchable, interpretable, and reusable biological knowledge.
01
Partitioned memory
02
Explicit retrieval
03
Gated interpretation
01 / Core thesis
A different foundation
DNA is not natural language.
The rules encoded in genomes often reside in local motifs, functional regions, and regulatory relationships across scales. Meno starts from the structure of biological sequences, so the model can show not only what it predicts, but also which knowledge it uses.
Meno architecture
From sequence to callable biological knowledge
Meno organizes sequence patterns outside model parameters as readable conditional memory, making knowledge formation, retrieval, and iteration more transparent.
Explicit retrieval of k-mers and motifs
For a new sequence, the model retrieves relevant patterns from memory and uses the same evidence for both prediction and interpretation.
Gates trace knowledge contribution
Gating signals reveal how different memories contribute to an output, helping researchers inspect the positions, patterns, and functional regions the model relies on.
Meno
Conditional memory architecture
Interpretable by design
Interpretability is built into the architecture
Rather than adding explanations after prediction, Meno preserves observable memory structures while learning and retrieving knowledge, creating clearer leads for biological validation.
Memory space
Memory space separates regional semantics
As memory units organize around sequence context, coding and non-coding regions develop distinct distributions, providing a window into the model's internal biological semantics.
Gate alignment
Gating signals align with functional regions
By examining the relationship between memory activation and functional sequence regions, researchers can formulate more specific and testable biological hypotheses.
One knowledge core
Explain, predict, and design with one knowledge core
Meno turns model outputs into genomic knowledge assets that can accumulate across tasks, datasets, and experimental cycles.
Explain
Explain why a sequence works
Identify local patterns, functional regions, and memory contributions that can inform experimental design.
Predict
Predict variant-function relationships
Combine sequence representations with retrievable memory for functional, phenotypic, and regulatory modeling.
Design
Design sequences for validation
Generate and rank candidate sequences around a target function, then update model knowledge with experimental feedback.
04 / Applications
From model to validation
From model research to real-world validation
Precision medicine research
Precision medicine research
Analyze variant function, disease-associated sequences, and candidate targets to help research teams narrow the experimental search space.
Synthetic biology
Synthetic biology
Discover functional elements, optimize sequences, and evaluate candidates while connecting computational design with experimental feedback.
Xenotransplantation CRISPR
CRISPR editing for xenotransplantation
Support animal editing programs, including semi-cloned pigs, across sgRNA ranking, donor/HDR design, PCR validation, and experimental feedback.
Note: The capabilities described on this website are intended for research and industrial R&D. They do not constitute medical diagnosis, treatment advice, or claims for a clinical product.
Learning loop
Every validation cycle expands the biological codebook
From task definition to experimental feedback, Meno connects models, data, and real biological systems through traceable knowledge iteration.
-
01
Define the task
Set the functional objective, species scope, and validation boundaries
-
02
Parse the sequence
Build task-specific sequence representations and regional context
-
03
Retrieve memory
Call interpretable motifs and conditional memory
-
04
Generate hypotheses
Produce rankable, comparable candidate results
-
05
Return experimental evidence
Update task knowledge and model capability with validation data
About Meno
One team across foundational technology, life science, and industrial validation.
Hangzhou Xupu Zhiqi Technology Co., Ltd. develops genome foundation models and interpretable sequence intelligence. Meno extends the research direction of explicit motif memory to build reusable biological knowledge infrastructure for a broader range of genomic tasks.
Seeking partners to validate the rules of life