Causal Reasoning and Transcriptomics: Advances with LLMs and Foundation Models

Advances in Causal Reasoning and Transcriptomics: A Focus on Large Language Models and Foundation Models

Recent developments in the field have seen significant advancements in the integration of causal reasoning with large language models (LLMs) and the application of foundation models in transcriptomics. The field is moving towards more robust and interpretable models, leveraging the strengths of both traditional methods and cutting-edge AI technologies.

In the realm of causal reasoning, there is a growing emphasis on embedding causality into the training process of LLMs to enhance their reliability and ethical alignment. This shift aims to move beyond correlation-driven paradigms, addressing issues such as demographic biases and hallucinations. Additionally, innovative approaches like CausalChat are emerging, which utilize LLMs to construct detailed causal networks through interactive interfaces, demonstrating the potential of AI in complex causal modeling.

Transcriptomics is also witnessing a transformation with the advent of foundation models tailored for perturbation analysis. These models are being rigorously benchmarked against classical techniques to identify those most effective in real-world scenarios. The integration of deep learning with transcriptomics data is opening new avenues for understanding biological perturbations and gene-gene interactions, with a focus on mitigating biases and improving data efficiency.

Noteworthy papers include:

  • CausalChat: Demonstrates the potential of LLMs in constructing detailed causal networks through interactive interfaces.
  • Benchmarking Transcriptomics Foundation Models: Identifies superior models for perturbation analysis, highlighting the importance of robust evaluation frameworks.
  • LLMScan: Introduces a novel causal inference-based monitoring technique for detecting LLM misbehavior, offering a comprehensive solution to potential risks.

Sources

Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all

CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models

A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference

Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning

HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph

Influence of Backdoor Paths on Causal Link Prediction

Causality for Large Language Models

Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery

LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation

CausalGraph2LLM: Evaluating LLMs for Causal Queries

Comprehensive benchmarking of large language models for RNA secondary structure prediction

LLMScan: Causal Scan for LLM Misbehavior Detection

Improving Causal Reasoning in Large Language Models: A Survey

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