AIMC Journal:
bioRxiv

Showing 1651 to 1660 of 4956 articles

Mechanistic learning to predict and understand minimal residual disease

bioRxiv May 6, 2026
Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their key advantage lies in interpretability of relationships between input parameters and outcomes of inter...
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PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes

bioRxiv May 6, 2026
Cellular reprogramming is a complex interplay between perturbations and regulatory elements, culminating in gene expression changes. Current computational approaches do not explicitly model these regulatory interactions. Here, we first performed comb...
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Prediction of Stimulation-Defined Eloquent Cortex Using Graph-Theoretical Connectivity from Electrocorticography During Presurgical Mapping

bioRxiv May 6, 2026
Background and Objectives: Electrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evaluation but is time-intensive, constrained by incomplete cortical sampling, and limited by patient tol...
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Tumor cell specific total mRNA expression informed neural networks predicts cancer progression

bioRxiv May 6, 2026
Inferring tumor molecular phenotypes from high-dimensional multi-omic data is a fundamental challenge in computational biology. Current methods for estimating tumor cell-specific total mRNA expression (TmS) require matched DNA and RNA sequencing data...
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An Open Reproducible Framework for CNN-Based Cetacean Vocalization Detection in Passive Acoustic Monitoring

bioRxiv May 6, 2026
This paper presents a six-stage methodological framework for Convolutional Neural Network (CNN)-based cetacean vocalization detection and classification in Passive Acoustic Monitoring (PAM), implemented as the open-source toolkit ai-pam-pipeline. The...
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Learning the Language of the Microbiome with Transformers

bioRxiv May 6, 2026
Self-supervised pretraining has become central to biological machine learning, yet microbiome data remains comparatively underexplored in terms of both modeling approaches and evaluation frameworks. To address this gap, we present Atlas, a pretrainin...
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UNKAI: A protein functional identity prediction model based on ESM-C latent representations and the attention mechanism

bioRxiv May 6, 2026
The rapid advancement of genome sequencing technologies has led to the accumulation of a vast number of protein sequences in public databases. However, a significant proportion of these proteins remain functionally uncharacterized. Concurrently, the ...
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Integrated Multi-Omics Analysis for the Identification of Disease-Associated Variations and Prognostic Biomarkers in Triple-Negative Breast Cancer (TNBC)

bioRxiv May 6, 2026
Background: Triple-negative breast cancer (TNBC) exhibits substantial molecular heterogeneity and lacks targeted receptor therapies. Single-omic approaches inadequately capture its regulatory complexity, necessitating integrated multi-omic frameworks...
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Claim-Level Transparency Analysis of LLM-Generated Diagnostic Reports: A Metabolic and Endocrine Biomarker Study

bioRxiv May 6, 2026
Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in generating patient-specific diagnostic reports remains sparse, particularly for laboratory interpretation...
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Evaluating Reference-Independent Pipelines for the Detection of Spreading Organisms in Metagenomic Datasets

bioRxiv May 6, 2026
The emergence of unidentified pathogens, or "Disease X," poses a significant threat to global health, necessitating the development of proactive surveillance strategies for the wildlife and human virosphere. Since novel viruses often lack universal g...
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