Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 134 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 24 tok/s Pro
GPT-5 High 23 tok/s Pro
GPT-4o 77 tok/s Pro
Kimi K2 159 tok/s Pro
GPT OSS 120B 431 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Thought Graph: Generating Thought Process for Biological Reasoning (2403.07144v1)

Published 11 Mar 2024 in cs.CL

Abstract: We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes. Our framework stands out for its ability to provide a deeper understanding of gene sets, significantly surpassing GSEA by 40.28% and LLM baselines by 5.38% based on cosine similarity to human annotations. Our analysis further provides insights into future directions of biological processes naming, and implications for bioinformatics and precision medicine.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (12)
  1. Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey. arXiv:2311.07914 [cs.CL]
  2. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25 (01 2000), 25–29.
  3. Graph of Thoughts: Solving Elaborate Problems with Large Language Models. arXiv:2308.09687 [cs.CL]
  4. Building a knowledge graph to enable precision medicine. Scientific Data 10, 1 (2023), 67.
  5. Evaluation of large language models for discovery of gene set function. arXiv:2309.04019 [q-bio.GN]
  6. Self-Alignment Pretraining for Biomedical Entity Representations. arXiv:2010.11784 [cs.CL]
  7. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102, 43 (2005), 15545–15550. https://doi.org/10.1073/pnas.0506580102 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.0506580102
  8. Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv:2203.11171 [cs.CL]
  9. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903 [cs.CL]
  10. MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models. arXiv:2308.09729 [cs.AI]
  11. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv:2305.10601 [cs.CL]
  12. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. arXiv:2205.10625 [cs.AI]
Citations (4)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.