Bio x AI Hackathon
  • Welcome to the Bio x AI Hackathon
  • Getting Started
    • Quickstart
    • Important Links
  • Developers
    • BioAgents
    • CoreAgents
    • Eliza Agent Framework
    • Knowledge Graphs
    • .cursorrules
    • Starter-repos
    • Plugin Guide
  • Vision and Mission
    • Bio x AI Hackathon
    • The Problems in Science
    • TechBio
    • Guidance from the Judges
      • Important Datasets and Code Repositories
      • Reading List
      • Common Mistakes for Developers new to Academia
    • Hackathon Ideas
      • Full Projects
        • The Complexity Slider - Finding Hypotheses at the Limits of Human Knowledge
        • [Hard Mode] Metadata Generation on datasets with No Manuscript or Code Associated
        • Inverse Reproducibility - Given Manuscript and Data, Make the Code
        • Atlas of Research Methods Formatted for Agentic Reuse
        • Utilizing Knowledge Graphs for the Detection of Potential Null Results
        • Creating an Iterative Publication Stack by Linking Together Existing Tooling
        • Longevity Atlas: Building a Decentralized Knowledge Network with Agentic Research Hypothesis Engine
        • CoreAgent Track - Opportunities to work with BioDAOs
        • SpineDAO Chronos Project Spec
      • Individual Plugins
        • Plug-ins for every piece of research tooling known to humankind
        • Reproducibility Assistant - Code Cleaning, Dockerization, etc
        • Finding and Differentiating Cardinal vs Supporting Assertions
        • [Easier Mode] Metadata Generation on Datasets Given the Manuscript and Code Repository
        • Sentiment Analysis on Existing Citations, Dissenting vs Confirming
        • Agentic Metadata Template Creation for Standard Lab Equipment
  • Ops
    • Calendar
      • Key Dates
      • Office Hours
    • Judges and Mentors
      • Communicating to Judges and Mentors
      • BioAgent Judging Panel
      • CoreAgent Judging Panel
      • Mentors
    • Prize Tracks
    • Hackathon Rules
    • Kickoff Speakers
    • FAQ
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On this page
  • AI & Automation in Science
  • Knowledge Graphs & Data Management
  • BioTech and TechBio Perspectives
  • Additional BioHack Reading Materials
  1. Vision and Mission
  2. Guidance from the Judges

Reading List

PreviousImportant Datasets and Code RepositoriesNextCommon Mistakes for Developers new to Academia

Last updated 1 month ago

This reading list provides a curated selection of resources related to the intersection of AI, biology, knowledge graphs, and the future of scientific research. Each entry is accompanied by contextual information and categorized to enhance understanding and facilitate deeper exploration.

AI & Automation in Science

  • Sakana AI Scientist Agents

    • Category: AI Agents, Scientific Automation

    • Context: Explores the development of AI scientist agents capable of autonomously conducting experiments and generating hypotheses.

  • AI Tools Are Spotting Errors in Research Papers: Inside a Growing Movement

    • Category: AI in Research, Error Detection

    • Context: Discusses the use of AI tools to identify errors in scientific research papers, improving accuracy and reliability.

  • BioAgents: Accelerating Decentralized Science with AI Agents

    • Category: AI Agents, Decentralized Science

    • Context: Discusses the use of AI agents to accelerate decentralized science, democratizing research.

Knowledge Graphs & Data Management

  • The Implicitome: A Resource for Rationalizing Gene-Disease Associations

    • Category: Knowledge Graphs, Gene-Disease Relationships

    • Context: Provides data and tools for understanding gene-disease associations, aiding in knowledge graph creation.

  • SciGraph: The Dawn of a New Scientific Era

    • Category: Knowledge Graphs, Scientific Data

    • Context: Explores SciGraph and its potential to create a comprehensive knowledge graph of scientific information.

  • FAIR Guiding Principles for Scientific Data Management and Stewardship

    • Category: Data Management, FAIR Principles

    • Context: Outlines the FAIR principles for managing scientific data, crucial for data quality and reusability.

  • FAIR Principles: Interpretations and Implementation Considerations

    • Category: Data Management, FAIR Implementation

    • Context: Offers practical guidance on implementing the FAIR principles in real-world research.

  • The Anatomy of a Nanopublication

    • Category: Data Sharing, Nanopublications

    • Context: Explains the structure and components of nanopublications, enhancing transparency and reproducibility.

  • The Comparative Anatomy of Nanopublications and FAIR Digital Objects

    • Category: Data Sharing, FAIR Digital Objects

    • Context: Compares nanopublications with FAIR Digital Objects, improving understanding of data sharing.

  • Percolation Theory

    • It mathematically studies connected clusters forming in random graphs or lattices, modeling phenomena like fluid flow through porous media.

    • Percolation analyzes phase transitions, identifying critical thresholds where large-scale connectivity suddenly emerges across the system.

    • In knowledge graphs, it helps optimize by identifying crucial connections and assessing network robustness or information diffusion pathways.

BioTech and TechBio Perspectives

  • The Techbio Idea Maze: To Be or Not (Shawn Dimaantha)

    • Category: Strategic Analysis, Techbio Landscape

    • Context: Explores the complexities and challenges of navigating the "idea maze" within the techbio field, emphasizing the need for innovative approaches.

  • Why We Haven't Made Breakthrough Medical Discoveries (Shawn Dimaantha)

    • Category: Medical Research, Innovation Challenges

    • Context: Delves into the reasons behind the stagnation of breakthrough medical discoveries, examining limitations in current research methodologies.

  • Vincent Alessi Podcast Interview

    • Category: Personal Insights, Career Perspectives

    • Context: Provides insights into Vincent Alessi's career and perspectives, relevant to a hackathon focused on knowledge graphs.

  • Bits in Bio Newsletter

    • Category: Current Events, Bio/AI Trends

    • Context: Vincent Alessi's newsletter offers current events and perspectives on the intersection of biology and AI.

Additional BioHack Reading Materials

https://sakana.ai/ai-scientist/
https://archive.ph/RekIL
https://www.bio.xyz/blog-posts/bioagents-pioneering-decentralized-science-with-ai-agents
https://archive.ph/jpULH
https://medium.com/id-theory/scigraph-the-dawn-of-a-new-scientific-era-d067e293c6b2
https://archive.ph/IQfrH
https://archive.ph/32Xcy
https://archive.ph/jdT1S
https://archive.ph/Mqqli
https://en.wikipedia.org/wiki/Percolation_theory
https://shawndimantha.substack.com/p/the-techbio-idea-maze-to-be-or-not
https://shawndimantha.substack.com/p/why-we-havent-made-breakthrough-medical-b28
https://podcasts.apple.com/us/podcast/vincent-alessis-advanced-materials-exit-and-journey/id1532346146?i=1000669661353&l=ar
https://bitsinbio.substack.com/
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Harmonizing quality measures of FAIRness assessment towards machine-actionable quality information
Discovering Datasets on the Web Scale: Challenges and Recommendations for Google Dataset Search
Knowledge Graphs as a source of trust for LLM-powered enterprise question answering
Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions
LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs
SPARQL Generation with Entity Pre-trained GPT for KG Question Answering
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
When Search Engine Services meet Large Language Models: Visions and Challenges
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
https://www.sciencedirect.com/science/article/pii/S1570826824000398?via%3Dihub
On the performativity of SDG classifications in large bibliometric databases
Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!