Research Paper Kb

research-kb/
  _index.yaml              # Master index of all papers
  _themes.yaml             # Cross-cutting themes and concepts
  _questions.yaml           # Active research questions
  papers/
    smith-2024-deep-learning-proteins/
      notes.md              # Structured paper notes
      claims.yaml           # Extracted claims with evidence
      figures/              # Saved key figures (optional)
    jones-2023-attention-mechanisms/
      notes.md
      claims.yaml
  syntheses/
    attention-in-biology.md  # Cross-paper synthesis documents
    methodology-comparison.md

---
paper_id: smith-2024-deep-learning-proteins
title: "Deep Learning for Protein Structure Prediction: A Survey"
authors: ["Smith, J.", "Chen, L.", "Williams, R."]
year: 2024
venue: "Nature Reviews Molecular Cell Biology"
doi: "10.1038/s41580-024-00001-1"
date_read: "2026-03-10"
relevance: high
tags: ["protein structure", "deep learning", "AlphaFold", "survey"]
connections: ["jones-2023-attention-mechanisms", "brown-2022-alphafold2"]
---

# Deep Learning for Protein Structure Prediction: A Survey

## Reading Purpose
Why I read this paper and what questions I hoped it would answer.

## Summary
2-3 paragraph summary of the paper's main argument and contribution.

## Key Findings
1. **Finding 1**: Description with page/section reference (p. 5, Section 3.2)
2. **Finding 2**: Description
3. **Finding 3**: Description

## Methodology Assessment
- **Approach**: Survey/review methodology
- **Scope**: 200+ papers covering 2018-2024
- **Strengths**: Comprehensive taxonomy of approaches, clear evaluation framework
- **Weaknesses**: Limited coverage of non-English literature, no meta-analysis
- **Reproducibility**: N/A (review paper)

## Connections to My Research
- Directly relevant to [my research question] because...
- Contradicts/supports [finding from another paper] in that...
- Suggests new direction: ...

## Key Quotes
> "Quote 1" (p. X)
> "Quote 2" (p. Y)

## Questions Raised
- [ ] Follow up on the claim that X leads to Y (cited as [ref])
- [ ] Check whether the benchmark in Table 3 includes recent models
- [ ] Read the methodological critique in [cited paper]

## References to Chase
- [Author, Year]: Reason this reference seems important
- [Author, Year]: Potential counterargument to main thesis

# claims.yaml - Extracted claims with evidence quality
claims:
  - id: smith-2024-claim-01
    statement: "AlphaFold2 achieves experimental-level accuracy on 95% of CASP14 targets"
    evidence_type: "empirical"
    evidence_quality: "strong"  # strong | moderate | weak | anecdotal
    page: 8
    section: "3.1"
    supports: ["brown-2022-claim-03"]
    contradicts: []
    caveats: "Accuracy measured by GDT-TS; performance varies for disordered regions"

  - id: smith-2024-claim-02
    statement: "Attention mechanisms are the key architectural innovation enabling structure prediction"
    evidence_type: "analytical"
    evidence_quality: "moderate"
    page: 12
    section: "4.2"
    supports: ["jones-2023-claim-01"]
    contradicts: ["lee-2023-claim-05"]
    caveats: "Author's interpretation; alternative architectures not fully explored"

import yaml
from pathlib import Path
from datetime import date

def add_paper(kb_path, paper_id, metadata, notes):
    """Add a new paper to the knowledge base."""
    paper_dir = Path(kb_path) / "papers" / paper_id
    paper_dir.mkdir(parents=True, exist_ok=True)

    # Write notes.md with YAML frontmatter
    frontmatter = yaml.dump(metadata, default_flow_style=False)
    content = f"---\n{frontmatter}---\n\n{notes}"

    (paper_dir / "notes.md").write_text(content)

    # Update master index
    update_index(kb_path, paper_id, metadata)

    print(f"Added paper: {paper_id}")

def update_index(kb_path, paper_id, metadata):
    """Update the master index with new paper."""
    index_path = Path(kb_path) / "_index.yaml"
    if index_path.exists():
        index = yaml.safe_load(index_path.read_text()) or {}
    else:
        index = {"papers": {}}

    index["papers"][paper_id] = {
        "title": metadata["title"],
        "year": metadata["year"],
        "relevance": metadata.get("relevance", "medium"),
        "tags": metadata.get("tags", []),
        "date_added": str(date.today())
    }

    index_path.write_text(yaml.dump(index, default_flow_style=False))

def find_papers_by_tag(kb_path, tag):
    """Find all papers with a given tag."""
    index = yaml.safe_load((Path(kb_path) / "_index.yaml").read_text())
    results = []
    for paper_id, info in index["papers"].items():
        if tag in info.get("tags", []):
            results.append((paper_id, info["title"]))
    return results

def find_connections(kb_path, paper_id):
    """Find all papers connected to a given paper."""
    paper_dir = Path(kb_path) / "papers" / paper_id
    notes_path = paper_dir / "notes.md"
    content = notes_path.read_text()

    # Parse YAML frontmatter
    parts = content.split("---", 2)
    metadata = yaml.safe_load(parts[1])

    return metadata.get("connections", [])

def get_claims_supporting(kb_path, claim_id):
    """Find all claims that support a given claim."""
    results = []
    for claims_file in Path(kb_path).rglob("claims.yaml"):
        data = yaml.safe_load(claims_file.read_text())
        for claim in data.get("claims", []):
            if claim_id in claim.get("supports", []):
                results.append(claim)
    return results

# Synthesis: Attention Mechanisms in Biology

## Theme Overview
How attention mechanisms from NLP have been adapted for biological sequence analysis.

## Contributing Papers
1. smith-2024: Survey covering 200+ papers on protein structure prediction
2. jones-2023: Original attention mechanism analysis
3. brown-2022: AlphaFold2 architecture deep dive

## Consensus Findings
- Attention enables capturing long-range dependencies in sequences
- Multi-head attention is more effective than single-head for structural prediction
- Pre-training on large unlabeled sequence databases is critical

## Contested Points
- Whether attention maps are interpretable (smith-2024 says yes, lee-2023 says no)
- Optimal number of attention heads (ranges from 8 to 64 in literature)

## Gaps in the Literature
- Limited comparison with non-attention architectures on equal compute budgets
- Few studies on attention for RNA structure prediction
- No theoretical analysis of why attention works for biological sequences