Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response

• URL: http://arxiv.org/abs/2603.02274v1
• Date: Sun, 01 Mar 2026 16:15:58 GMT
• Title: Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response
• Abstract summary: Precision oncology is currently limited by the small-N, large-P paradox.<n>We present a Neuro-Symbolic Agentic Framework that bridges this gap.<n>Our framework provides a transparent, biologically grounded path towards explainable AI in cancer research.
• Score: 4.796382757669091
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant, but high-quality drug response samples are often sparse. While deep learning models achieve high predictive accuracy, they remain black boxes that fail to provide the causal mechanisms required for clinical decision-making. We present a Neuro-Symbolic Agentic Framework that bridges this gap by integrating a quantitative machine learning World Model with an LLM-based agentic reasoning layer. Our system utilises a forensic data pipeline built on the Sanger GDSC dataset (N=83), achieving a robust predictive correlation (r=0.504) and a significant performance gain through the explicit modelling of clinical context, specifically Microsatellite Instability (MSI) status. We introduce the concept of Inverse Reasoning, where the agentic layer performs in silico CRISPR perturbations to predict how specific genomic edits, such as APC or TP53 repair, alter drug sensitivity. By distinguishing between therapeutic opportunity and contextual resistance, and validating these findings against human clinical data (p=0.023), our framework provides a transparent, biologically grounded path towards explainable AI in cancer research.

Related papers

• Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions [4.432106960947219]
  We present ILLUME+, a scalable post-hoc explainability framework that moves beyond single-gene assessments to capture multiple forms of explanation.<n>ILLUME+ produces more stable gene importance scores than existing baselines, recovers established drug-gene associations and mechanisms of action, and enables AI-assisted hypothesis generation to uncover novel interaction-driven molecular signals in cancer biology.
  arXiv  Detail & Related papers  (2026-07-01T13:34:04Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
  Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
  arXiv  Detail & Related papers  (2026-03-31T02:36:10Z)
• A WDLoRA-Based Multimodal Generative Framework for Clinically Guided Corneal Confocal Microscopy Image Synthesis in Diabetic Neuropathy [8.701084151107652]
  Corneal Confocal Microscopy is a sensitive tool for assessing small-fiber damage in Diabetic Peripheral Neuropathy (DPN)<n>Development of robust, automated deep learning-based diagnostic models is limited by scarce labelled data and fine-grained variability in corneal nerve morphology.<n>We propose a Weight-Decomposed Low-Rank Adaptation (WDLoRA)-based multimodal generative framework for clinically guided CCM image synthesis.
  arXiv  Detail & Related papers  (2026-02-14T09:32:44Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Interpretable Clinical Classification with Kolgomorov-Arnold Networks [70.72819760172744]
  Kolmogorov-Arnold Networks (KANs) offer intrinsic interpretability through transparent, symbolic representations.<n>KANs support built-in patient-level insights, intuitive visualizations, and nearest-patient retrieval.<n>These results position KANs as a promising step toward trustworthy AI that clinicians can understand, audit, and act upon.
  arXiv  Detail & Related papers  (2025-09-20T17:21:58Z)
• Clinically-guided Data Synthesis for Laryngeal Lesion Detection [2.573786844054239]
  This study introduces a novel approach that exploits a Latent Diffusion Model (LDM) coupled with a ControlNet adapter to generate laryngeal endoscopic image-annotation pairs.<n>The proposed approach can be leveraged to expand training datasets for CADx/e models, empowering the assessment process in laryngology.
  arXiv  Detail & Related papers  (2025-08-08T09:55:54Z)
• Mechanistic Interpretability of LoRA-Adapted Language Models for Nuclear Reactor Safety Applications [0.0]
  This paper presents a novel methodology for interpreting how Large Language Models encode and utilize domain-specific knowledge.<n>We adapted a general-purpose LLM to the nuclear domain using a parameter-efficient fine-tuning technique known as Low-Rank Adaptation.<n>By comparing the neuron activation patterns of the base model to those of the fine-tuned model, we identified a sparse set of neurons whose behavior was significantly altered.
  arXiv  Detail & Related papers  (2025-07-14T05:17:41Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents [47.640779069547534]
  AutoCT is a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning.<n>We show that AutoCT performs on par with or better than SOTA methods on clinical trial prediction tasks within only a limited number of self-refinement iterations.
  arXiv  Detail & Related papers  (2025-06-04T11:50:55Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Interpretable Droplet Digital PCR Assay for Trustworthy Molecular Diagnostics [6.936364565330349]
  I2ddPCR is a comprehensive framework integrating front-end predictive models (for droplet segmentation and classification) with GPT-4o multimodal large language model (MLLM)<n>This approach surpasses the state-of-the-art models, affording 99.05% accuracy in processing complex ddPCR images with varying signal-to-noise ratios (SNRs)
  arXiv  Detail & Related papers  (2025-01-16T00:33:17Z)
• Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses [40.80133767939435]
  Existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. We introduce VETE, a novel neural network framework that incorporates a variational component to mitigate noise effects. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research.
  arXiv  Detail & Related papers  (2024-07-05T13:13:02Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.