v1.0 to git:20260331.51dcb8c

+ description: "Systematic strategies for searching, retrieving, and analyzing scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers the PICO framework for clinical question formulation, three-tiered search strategy (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH vocabulary, boolean query construction, and full-text extraction workflows. Consult this guide when planning a literature search, constructing database queries, or deciding which search tier to use for a given research question."

+ # Scientific Literature Search

+ Scientific literature search is the foundation of evidence-based research. A well-executed search maximizes recall (finding all relevant papers) while maintaining precision (avoiding irrelevant results). This guide provides a systematic approach that combines database-specific query strategies, AI-assisted synthesis, and direct content extraction, organized into a three-tiered framework that scales from targeted lookups to comprehensive landscape reviews.

+ ## Key Concepts

+ ### The PICO Framework

+ For clinical and biomedical questions, structure queries using the PICO framework:

+ - **P** (Population): Who are you studying? (e.g., "Diabetes Mellitus"[MeSH])

+ - **I** (Intervention): What treatment or exposure? (e.g., "Metformin"[MeSH])

+ - **C** (Comparison): What is the alternative? (e.g., placebo, standard care)

+ - **O** (Outcome): What result are you measuring? (e.g., "Cardiovascular Diseases"[MeSH])

+ PICO queries can be combined with publication type filters to target specific evidence levels:

+ ```

+ "Diabetes Mellitus"[MeSH] AND "Metformin"[MeSH] AND "Cardiovascular Diseases"[MeSH] AND ("clinical trial"[Publication Type] OR "meta-analysis"[Publication Type])

+ ```

+ ### Three-Tiered Search Strategy

+ Literature search is most effective when approached in tiers of increasing breadth:

+ **Tier 1 -- Database-Specific Searches (Most Reliable)**

+ Query established academic databases (PubMed, arXiv, Google Scholar) for peer-reviewed, indexed content. This is the most reliable tier and should always be the starting point.

+ - PubMed (`query_pubmed`): Primary database for biomedical and life science literature. Supports MeSH controlled vocabulary and advanced field tags.

+ - arXiv (`query_arxiv`): Preprint server for physics, mathematics, computer science, and quantitative biology. Results appear faster than peer-reviewed journals.

+ - Google Scholar (`query_scholar`): Broadest coverage across all academic disciplines. Note: has aggressive rate limits on automated queries.

+ Best for: finding specific papers, systematic reviews, clinical evidence, preprints.

+ **Tier 2 -- AI-Assisted Web Search (Comprehensive)**

+ Use AI tools (`advanced_web_search_claude`) to synthesize broader context, identify research trends, and surface recent developments not yet indexed in databases. Also use general web search (`search_google`) for protocols, tutorials, and software documentation.

+ Best for: understanding the research landscape, complex multi-faceted questions, finding recent developments, identifying key researchers.

+ Avoid for: specific paper lookups (use Tier 1), citation counts (use Google Scholar), systematic reviews requiring reproducibility, searches where exact query terms must be documented.

+ **Tier 3 -- Direct Content Extraction (Deep Dive)**

+ Extract and analyze full-text content, PDFs, and supplementary materials from identified papers using `extract_url_content`, `extract_pdf_content`, and `fetch_supplementary_info_from_doi`.

+ Best for: detailed methodology extraction, data retrieval, protocol identification, supplementary data access.

+ ### PubMed Field Tags

+ PubMed supports field-specific searching to improve precision:

+

+ | `[MeSH]` | Medical Subject Heading (controlled vocabulary) | `"Neoplasms"[MeSH]` |

+ | `[Title]` | Title field only | `"CRISPR"[Title]` |

+ | `[Title/Abstract]` | Title or abstract | `"gene therapy"[Title/Abstract]` |

+ | `[Author]` | Author name | `"Zhang F"[Author]` |

+ | `[Journal]` | Journal name | `"Nature"[Journal]` |

+ | `[Publication Type]` | Article type filter | `"Review"[Publication Type]` |

+ | `[Date - Publication]` | Publication date range | `"2020/01/01"[Date - Publication]:"2024/12/31"[Date - Publication]` |

+ | `[MeSH Major Topic]` | MeSH term as major focus of the article | `"CRISPR-Cas Systems"[MeSH Major Topic]` |

+ ### Boolean Operators

+ Boolean operators control how search terms combine:

+

+ # AND: All terms must be present -- narrows results

+ # OR: Any term can be present -- broadens results (use for synonyms)

+ # NOT: Exclude terms -- use sparingly to avoid losing relevant papers

+ Use parentheses to group OR terms together before combining with AND.

+ ### arXiv Subject Categories

+ arXiv organizes preprints by subject category. Biology-related categories include:

+ ## Decision Framework

+ Use this tree to determine which search tier and database to start with:

+ What type of question are you answering?

+ ├── Clinical / biomedical question

+ │ ├── Specific drug or treatment → Tier 1: PubMed with PICO query

+ │ ├── Disease mechanism → Tier 1: PubMed with MeSH terms

+ │ └── Clinical trial evidence → Tier 1: PubMed filtered by Publication Type

+ ├── Computational / quantitative methods

+ │ ├── ML model or algorithm → Tier 1: arXiv (cs.LG, cs.AI)

+ │ ├── Computational biology method → Tier 1: arXiv (q-bio.*) + PubMed

+ │ └── Software tool or pipeline → Tier 2: AI-assisted web search

+ ├── Broad research landscape

+ │ ├── Current state of a field → Tier 2: AI-assisted web search

+ │ ├── Recent developments (last 6 months) → Tier 2: AI-assisted web search

+ │ └── Cross-disciplinary question → Tier 1: Google Scholar + Tier 2

+ ├── Specific paper or data

+ │ ├── Known paper details → Tier 1: any database by title/author/DOI

+ │ ├── Methodology or protocol → Tier 3: full-text extraction

+ │ └── Supplementary data → Tier 3: DOI-based supplementary fetch

+ └── Protocols / reagents

+ ├── Lab protocol → Tier 2: web search for protocols.io, etc.

+ └── Validated reagents → Tier 2: AI-assisted web search

+ | Scenario | Recommended Tier and Database | Rationale |

+ |----------|-------------------------------|-----------|

+ | Systematic review of clinical evidence | Tier 1: PubMed with MeSH + publication type filters | Reproducible, documented search strategy required |

+ | Finding a preprint on a new ML method | Tier 1: arXiv with category and keyword search | Preprints appear on arXiv before journals |

+ | Understanding the research landscape | Tier 2: AI-assisted web search | Requires synthesis across many sources |

+ | Extracting a specific protocol from a paper | Tier 3: PDF content extraction | Need full-text access to methods section |

+ | Finding papers across disciplines | Tier 1: Google Scholar | Broadest coverage across fields |

+ | Identifying key researchers in a niche area | Tier 2: AI-assisted web search | Requires contextual synthesis |

+ | Downloading supplementary data tables | Tier 3: DOI-based supplementary fetch | Direct access to supplementary files |

+ ## Best Practices

+ 1. **Use controlled vocabulary (MeSH) for PubMed searches**: Free-text searches miss papers that use different terminology. MeSH terms map synonyms to a single concept, improving recall without sacrificing precision.

+ ```python

+ # Free text misses synonyms

+ query_pubmed("heart attack treatment")

+ # MeSH captures all synonyms

+ query_pubmed('"Myocardial Infarction"[MeSH] AND "Drug Therapy"[MeSH]')

+ ```

+ 2. **Include synonyms and alternative terms with OR**: Scientific concepts often have multiple names (e.g., tumor/tumour/neoplasm). Group synonyms with OR inside parentheses to avoid missing relevant papers.

+ ```python

+ query_pubmed("(myocardial infarction OR heart attack) AND (treatment OR therapy)")

+ ```

+ 3. **Use phrase searching for multi-word concepts**: Quoting exact phrases prevents the search engine from splitting terms and matching them independently.

+ ```python

+ query_pubmed('"single cell RNA sequencing" AND methods')

+ ```

+ 4. **Filter by publication type when seeking specific evidence**: Clinical trials, systematic reviews, and meta-analyses each answer different questions. Use `[Publication Type]` to target the evidence level you need.

+ ```python

+ query_pubmed("COVID-19 vaccine efficacy AND clinical trial[Publication Type]")

+ ```

+ 5. **Start broad, then narrow iteratively**: Begin with core concepts (2-3 terms) and review initial results. Add specificity based on what you find -- more terms, date ranges, field tags, or publication types.

+ ```python

+ # Step 1: Broad

+ results = query_pubmed("CRISPR base editing iPSC", max_papers=20)

+ # Step 2: Add MeSH and specificity

+ results = query_pubmed(

+ '"CRISPR-Cas Systems"[MeSH] AND "base editing" AND "induced pluripotent stem cells" AND efficiency',

+ max_papers=20

+ )

+ # Step 3: Filter by date

+ results = query_pubmed(

+ '"CRISPR-Cas Systems"[MeSH] AND "base editing" AND "induced pluripotent stem cells" AND efficiency AND ("2022"[Date - Publication]:"2024"[Date - Publication])',

+ max_papers=20

+ )

+ ```

+ 6. **Cross-reference multiple databases**: No single database covers all literature. Use PubMed for biomedical content, arXiv for computational preprints, and Google Scholar for cross-disciplinary coverage.

+ 7. **Assess result quality systematically**: Evaluate papers for source reliability (peer-reviewed journal), author credentials, recency, study design appropriateness, sample size adequacy, reproducibility, declared conflicts of interest, and citation count.

+ ## Common Pitfalls

+ 1. **Overly long and specific queries**: Packing too many terms into a single query causes missed results because all terms must match simultaneously.

+ - *How to avoid*: Limit queries to core concepts (3-5 terms). Run separate searches for sub-topics and combine results manually.

+ ```python

+ # Too specific -- misses relevant papers

+ query_pubmed("CRISPR Cas9 gene editing HEK293T cells 2024 efficiency optimization delivery")

+ # Better -- core concepts only

+ query_pubmed("CRISPR Cas9 gene editing optimization efficiency")

+ ```

+ 2. **Relying on a single database**: PubMed has biomedical focus, arXiv covers preprints, Google Scholar spans disciplines. Using only one database guarantees blind spots.

+ - *How to avoid*: Always search at least two databases. For computational biology, combine PubMed and arXiv. For cross-disciplinary topics, include Google Scholar.

+ 3. **Ignoring publication dates**: Scientific knowledge evolves rapidly. Foundational papers remain relevant, but methods and clinical evidence may be superseded.

+ - *How to avoid*: Check publication dates in all results. For methods papers, prefer the last 3-5 years. For foundational concepts, older papers are acceptable but verify with recent reviews.

+ 4. **Skipping title and abstract review before deep-diving**: Not all search results that match keywords are actually relevant. Downloading and reading full texts without screening wastes time.

+ - *How to avoid*: Always screen titles and abstracts first. Only extract full text (Tier 3) for papers that pass screening.

+ 5. **Using NOT operators too aggressively**: The NOT operator can inadvertently exclude relevant papers that mention the excluded term in a different context.

+ - *How to avoid*: Use NOT sparingly. Prefer adding positive terms to narrow results rather than excluding terms. When you must use NOT, verify that excluded results are genuinely irrelevant.

+ 6. **Ignoring Google Scholar rate limits**: Google Scholar aggressively rate-limits automated queries, which can block further searches.

+ - *How to avoid*: Use Google Scholar sparingly. Add delays between requests. Prefer PubMed or arXiv for bulk searching and reserve Google Scholar for cross-disciplinary checks.

+ 7. **Not documenting the search strategy**: For systematic reviews and reproducible research, an undocumented search cannot be verified or reproduced.

+ - *How to avoid*: Record your search terms, databases queried, date ranges, and number of results at each stage. This is essential for systematic reviews and good practice for all searches.

+ ## Workflow

+ 1. **Step 1: Define the research question**

+ - Identify the main concept, population/model, intervention/method, desired outcome, and time frame

+ - For clinical questions, map to the PICO framework

+ - Example: "Find recent papers on CRISPR base editing efficiency in human iPSCs" decomposes to: main concept = CRISPR base editing, model = human iPSCs, outcome = efficiency, time frame = last 3 years

+ 2. **Step 2: Construct and execute database queries (Tier 1)**

+ - Start with PubMed for biomedical topics, arXiv for computational topics

+ - Begin with a broad query using 2-3 core terms

+ - Refine with MeSH terms, field tags, date filters, and publication type filters

+ ```python

+ from biomni.tool.literature import query_pubmed, query_arxiv, query_scholar

+ # PubMed: biomedical literature

+ results = query_pubmed(

+ '"CRISPR-Cas Systems"[MeSH] AND "Gene Editing"[MeSH]',

+ max_papers=20

+ )

+ # arXiv: computational biology preprints

+ results = query_arxiv("protein structure prediction", max_papers=10)

+ # Google Scholar: broad cross-disciplinary coverage

+ result = query_scholar("single cell RNA sequencing analysis methods")

+ 3. **Step 3: Supplement with AI-assisted search (Tier 2)**

+ - Use AI-assisted web search for landscape overviews and recent developments

+ - Use general web search for protocols, tutorials, and documentation

+ from biomni.tool.literature import advanced_web_search_claude

+ results = advanced_web_search_claude(

+ "What are the latest developments in CAR-T cell therapy for solid tumors in 2024?",

+ max_searches=3

+ )

+ 4. **Step 4: Evaluate and filter results**

+ - Screen titles and abstracts for relevance

+ - Prioritize by recency, journal quality, citation count, and study design

+ - For clinical evidence, prioritize RCTs, systematic reviews, and meta-analyses

+ - For methods, prioritize protocol papers and method comparisons

+ - Decision point: If too many results, add more specific terms or filters. If too few, broaden terms and add synonyms.

+ 5. **Step 5: Deep dive into key papers (Tier 3)**

+ - Extract full text from high-priority papers

+ - Download supplementary materials for data and protocols

+ - Check reference lists for additional relevant papers

+ ```python

+ from biomni.tool.literature import extract_url_content, extract_pdf_content, fetch_supplementary_info_from_doi

+ # Extract article content from URL

+ content = extract_url_content("https://www.nature.com/articles/nature12373")

+ # Extract text from PDF

+ content = extract_pdf_content("https://arxiv.org/pdf/1706.03762.pdf")

+ # Download supplementary files using DOI

+ log = fetch_supplementary_info_from_doi(

+ "10.1038/nature12373",

+ output_dir="./supplementary_materials"

+ )

+ 6. **Step 6: Document and iterate**

+ - Record all search terms, databases, filters, and result counts

+ - If gaps remain, revisit Steps 2-3 with refined queries

+ - For systematic reviews, follow PRISMA guidelines for reporting

+ ## Common Search Scenarios

+ The following scenarios illustrate how to combine the three tiers for typical research questions.

+ ### Finding Methods and Protocols

+ Start with PubMed for published methodology papers, then supplement with web search for step-by-step protocols from resources like protocols.io.

+ from biomni.tool.literature import query_pubmed, search_google

+

+ # Search for methodology papers in PubMed

+ ### Understanding Disease Mechanisms

+ Begin with review articles for a broad overview, then drill into specific mechanistic studies.

+

+ # Find review articles first for an overview

+ ### Finding Drug and Treatment Information

+ Use publication type filters to separate clinical trial evidence from systematic reviews.

+

+ # Clinical trials for a specific drug-condition pair

+ # Systematic reviews and meta-analyses

+ ### Tracking Latest Developments

+ Combine AI-assisted search for synthesis with database searches for recent indexed publications.

+

+ from biomni.tool.literature import advanced_web_search_claude, query_pubmed

+

+ # AI-assisted synthesis of recent advances

+ # Supplement with recent PubMed results

+ ### Finding Specific Reagents and Materials

+ Use AI-assisted search for validated reagent recommendations, supplemented by general web search.

+

+ from biomni.tool.literature import advanced_web_search_claude, search_google

+

+ # Search supplier databases

+ ### Comparative Analysis Across Methods

+ Use AI-assisted search for synthesized comparisons of techniques or tools.

+ ```python

+ from biomni.tool.literature import advanced_web_search_claude

+ # Compare approaches with AI synthesis

+ results = advanced_web_search_claude(

+ "Compare different CRISPR delivery methods for in vivo gene editing: viral vectors vs lipid nanoparticles",

+ max_searches=5

+ )

+ ```

+ ## Quality Assessment Checklist

+ When evaluating search results, apply these criteria:

+ - **Source reliability**: Is the paper from a peer-reviewed journal?

+ - **Author credentials**: Are the authors established experts in the field?

+ - **Recency**: Is the information current enough for your purpose?

+ - **Study design**: Is the design appropriate for the question (e.g., RCT for efficacy, cohort for risk)?

+ - **Sample size**: Is it adequate for the conclusions drawn?

+ - **Reproducibility**: Are methods described clearly enough to replicate?

+ - **Conflicts of interest**: Are any conflicts declared?

+ - **Citation count**: Has the paper been well-cited by subsequent work?

+ ## Further Reading

+ - [PubMed Help](https://pubmed.ncbi.nlm.nih.gov/help/) -- Official guide to PubMed search syntax, field tags, filters, and advanced features

+ - [arXiv Help Pages](https://info.arxiv.org/help/index.html) -- Documentation on arXiv search, subject categories, and submission process

+ - [MeSH Browser](https://meshb.nlm.nih.gov/) -- NLM tool for browsing and searching the Medical Subject Headings controlled vocabulary

+ - [PRISMA Statement](http://www.prisma-statement.org/) -- Guidelines for transparent reporting of systematic reviews and meta-analyses

+ - [Cochrane Handbook for Systematic Reviews](https://training.cochrane.org/handbook) -- Gold-standard methodology for systematic literature reviews

+ ## Related Skills

+ - `pubmed-database` -- Direct PubMed API access for programmatic literature retrieval

+ - `scientific-manuscript-writing` -- Structuring literature review sections within manuscripts

+ - `research-question-formulation` -- Frameworks for defining answerable research questions