Reading List
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.
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