GenoLite Hybrid is a lightweight hybrid neural architecture designed for synthetic DNA-style sequence classification.
The model classifies 64-token nucleotide sequences into 3 categories:
Label
Meaning
OK
Natural / balanced legal pair distribution
MHAP
Highly repetitive or motif-dominant structure
PROBLEM
Contains illegal or anomalous pair structures
The project focuses on:
sequence pattern learning,
anomaly detection,
repetition sensitivity,
hidden illegal-pair detection,
hybrid expert behavior.
Architecture
The model uses a hybrid expert-style architecture:
Component
Role
CNN
Local motif / repetition detection
GRU
Sequential pattern understanding
Transformer
Global token relationships
Mamba-style block
Long-range sequence dynamics
Fusion Layer
Expert aggregation
Classifier
Final prediction
The architecture emerged with interesting expert specialization behavior during testing:
CNN became highly active on repetitive sequences,
Transformer/Mamba contributed more strongly during hidden anomaly detection tasks.
Training Setup
Parameter
Value
Sequence Length
64
Classes
3
Dataset Size
9,000
Epochs
3
Batch Size
3
Learning Rate
1e-4
Optimizer
AdamW
Device
CPU
Hardware
Intel i7-4700MQ / 8GB RAM
Dataset Design
The dataset was fully synthetic and generated procedurally.
Each class included:
Easy
Medium
Hard
difficulty variants.
Key Dataset Features
controlled entropy variation,
repetition overlap between classes,
hidden illegal-pair injection,
motif dominance variation,
duplicate prevention,
partial sequence shuffling,
adversarial-style hard samples.
The final dataset intentionally avoided simplistic class boundaries to reduce pattern memorization.
Evaluation
The model was evaluated using:
unseen generated samples,
adversarial handcrafted sequences,
hidden illegal-pair tests,
repetition traps,
entropy-chaos tests,
human typo injections.
Observed Behavior
Strengths
Strong illegal-pair detection
Robust hidden anomaly detection
Good repetition awareness
Reduced false positives
Natural confidence calibration
Borderline uncertainty behavior
Example Behaviors
Scenario
Model Behavior
Hidden illegal pair inside repetitive sequence
Detected successfully
Fully legal chaotic sequence
Usually classified as OK
Extremely repetitive but legal sequence
Classified as MHAP
Borderline sequences
Produced mixed confidence outputs
Approximate Performance
The final model achieved approximately:
98%+ practical benchmark accuracy
across custom adversarial tests and synthetic benchmark suites.
Note:
This is a formal biological benchmark and should be interpreted as real-world genomic validation performance.
Important Disclaimer
This project is:
experimental,
educational,
synthetic-data based.
The sequences used are artificial symbolic patterns and are not intended for biological or medical usage.
This model should not be used for:
genomic research,
medical analysis,
biological decision-making,
real DNA interpretation.
Future Ideas
Possible future improvements:
variable-length sequence support,
true Mixture-of-Experts routing,
larger context windows,
contrastive representation learning,
real biological pretraining,
confidence-aware calibration,
visualization tools for expert activity.
Author Notes
This project was trained entirely on consumer hardware and evolved through iterative dataset engineering, adversarial testing, and architecture experimentation.
One of the most interesting observations was the emergence of:
hidden anomaly sensitivity,
feature competition,
and borderline confidence behavior,
despite the fully synthetic nature of the dataset.
