Datasets:

Milad96
/

Kluyveromyces-marxianus-chunks

license: apache-2.0
task_categories:
  - text-classification
  - token-classification
  - text-generation
language:
  - en
tags:
  - genomics
  - bioinformatics
  - kluyveromyces-marxianus
  - biobert
  - functional-genomics
  - pangenome
  - yeast
  - biomistral
size_categories:
  - 10K<n<100K

🧬 Kluyveromyces marxianus - BioBERT-Optimized Genomics Dataset

📋 Overview

2 semantically-optimized chunks for BioBERT fine-tuning, processed with BiOMistral-7B quantum chunking engine.

Key Features

✅ Deep Semantic Coherence (avg: 1.000) ✅ Optimal Token Size (~512 tokens per chunk) ✅ Quality Filtering (2 high-quality chunks) ✅ Entity Extraction (genes, proteins, pathways, conditions) ✅ BioBERT-Ready (1 chunks ≤512 tokens)

🎯 Research Context

PhD Thesis: Functional Genomics of Robust Linear Yeasts (Kluyveromyces marxianus) using BioBERT and Pangenome Methodology for Identifying Key Survival Genes in Severe Gut Conditions

Applications

🧬 Gene survival mechanism discovery
🔬 Metabolic pathway analysis
🦠 Stress response characterization
📊 Pangenome comparative genomics
🤖 AI-driven gene function prediction

📊 Dataset Statistics

Metric	Value
Total Chunks	2
High-Quality (≥0.7)	2 (100.0%)
BioBERT-Ready	1 (50.0%)
Avg Coherence	1.0000
Avg Density	0.7108
Avg Quality	0.8554
Processing Date	2025-11-01T08:29:54.885911

🧬 Data Structure

{
  "chunk_id": "train_0_0",
  "global_id": 0,
  "text": "Optimized text content...",
  "token_count": 487,
  "semantic_coherence": 0.8234,
  "information_density": 0.7156,
  "quality_score": 0.7695,
  "entities": {
    "genes": ["ABC1", "XYZ2"],
    "proteins": ["hexokinase"],
    "pathways": ["glycolysis"],
    "organisms": ["marxianus"],
    "conditions": ["acid", "gut"]
  },
  "biobert_ready": true
}

📁 Files

chunks_complete.json - All chunks with metadata (2 chunks)
chunks_high_quality.json - Premium subset (2 chunks)
chunks.jsonl - Streaming format
statistics.json - Processing statistics
analysis_dashboard.png - Visual analytics
interactive_dashboard.html - Interactive Plotly dashboard
entities_wordcloud.png - Entity visualization

🚀 Quick Start

Load Dataset

import json

# High-quality subset
with open('chunks_high_quality.json', 'r') as f:
    chunks = json.load(f)

print(f"Loaded {len(chunks):,} high-quality chunks")

BioBERT Fine-tuning

from transformers import BertTokenizer, BertForSequenceClassification

model = BertForSequenceClassification.from_pretrained(
    "dmis-lab/biobert-large-cased-v1.1",
    num_labels=YOUR_CLASSES
)

tokenizer = BertTokenizer.from_pretrained(
    "dmis-lab/biobert-large-cased-v1.1"
)

# Chunks are already 512-token optimized!
for chunk in chunks:
    encoding = tokenizer(
        chunk['text'],
        truncation=True,
        max_length=512,
        padding='max_length',
        return_tensors='pt'
    )
    # Train your model...

Filter by Quality

# Get excellent chunks (≥0.8)
excellent = [c for c in chunks if c['quality_score'] >= 0.8]

# Get gut-stress related chunks
gut_chunks = [
    c for c in chunks
    if any(cond in c['entities']['conditions']
           for cond in ['gut', 'acid', 'bile'])
]

📈 Quality Metrics

Semantic Coherence (0-1): Thematic consistency using BiOMistral embeddings Information Density (0-1): Vocabulary diversity (unique/total words) Quality Score (0-1): Combined metric (coherence + density) / 2

Quality Tiers

Excellent (≥0.8): Highly focused, rich content
Good (0.7-0.8): Strong coherence and diversity
Acceptable (0.5-0.7): Moderate quality
Low (<0.5): Basic content

🔬 Processing Pipeline

BiOMistral-7B Loading - 4-bit/FP16 quantization
Semantic Analysis - Deep understanding via embeddings
Intelligent Chunking - Boundary detection at natural breaks
Entity Extraction - Automatic identification
Quality Scoring - Multi-metric assessment
Visualization - Interactive and static analytics

🎓 Citation

@dataset{kmx_chunks_2024,
  title = {Kluyveromyces marxianus BioBERT-Optimized Chunks},
  author = {Quantum Chunking System},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/Milad96/Kluyveromyces-marxianus-chunks}
}

📜 License

Apache 2.0 - Free for commercial and research use

🤝 Source

Original Dataset: Milad96/Kluyveromyces-marxianus

Processing: BiOMistral-7B Quantum Chunking System

🔗 Resources

Generated: 2025-11-01 08:30:12 System: BiOMistral Quantum Chunking v1.0 Target: BioBERT-Large Fine-tuning