AIMC Journal:
bioRxiv

Showing 531 to 540 of 4935 articles

SLIM: A small linear model with STRING embeddings for single-cell genetic perturbation prediction

bioRxiv Aug 7, 2026
Predicting cellular responses to genetic perturbations is central to understanding gene function and prioritizing therapeutic targets, but experimental screens cannot exhaustively cover genes, cell types, and perturbation combinations. Recent benchma...
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A confound-diagnostic toolkit for in silico perturbation with single-cell foundation models

bioRxiv Aug 7, 2026
Deleting a gene token from a cell's input sequence offers a convenient native strategy for in silico perturbation, but the resulting embedding delta may not represent a biological knockout response. Apparent effects can instead reflect gene identity,...
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Benchmarking single-cell foundation models in a zero-shot setting

bioRxiv Aug 7, 2026
Single-cell foundation models have recently emerged as a promising approach for learning general-purpose representations from large-scale transcriptomic data. These models are trained on millions of cells and are designed to transfer their learned re...
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Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

bioRxiv Aug 7, 2026
The continuous accumulation of genetic mutations in influenza A viruses (IAVs) drives antigenic drift, necessitating precise antigenic prediction for optimal vaccine strain selection. While sequence-based methods have advanced antigenic surveillance,...
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Evaluating Lightweight and Full Fine-Tuning Strategies Against Classical Machine Learning for Protein Function Prediction

bioRxiv Aug 7, 2026
Motivation Protein language models (PLMs) have emerged as powerful tools for sequence-based prediction of protein function, yet systematic benchmarks comparing frozen embeddings, fine-tuning strategies like Low-Rank Adaptation (LoRA) and classical ma...
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ClinOracle: Hierarchical AI Prediction of Target Binding and Patient-Derived Functional Activity Across Diverse Therapeutic Targets

bioRxiv Aug 7, 2026
AI platforms for drug discovery routinely achieve high hit rates against biochemical targets, yet the central translational challenge remains predicting whether a compound will be functionally active in patient-derived human cells. Here, we present C...
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REFCON: Reference-free and robust copy number inference in single-cell tumor transcriptomes

bioRxiv Aug 7, 2026
Single-cell RNA sequencing (scRNA-seq) is widely used to infer copy number profiles from tumor cells. Existing methods build on a reference-based normalization paradigm: normalizing each tumor cell against a reference of normal cells, whether supplie...
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Multimodal neuroimaging-microbiota integration identifies Akkermansia as a modulator of alcohol-induced gut-liver-brain pathology

bioRxiv Aug 7, 2026
Alcohol use disorder (AUD) disrupts the gut-liver-brain axis, yet mechanistically grounded and therapeutically actionable targets within this network remain poorly defined. To identify microbial modulators of alcohol-induced tissue pathology, longitu...
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RiboRep: Replicate-Aware Cross-Modal Transformers for Codon-Resolved Ribosome Density Prediction

bioRxiv Aug 7, 2026
Ribosome profiling enables genome-wide measurement of translation at nucleotide resolution and provides a dynamic view of cellular protein synthesis under diverse biological conditions. Existing computational approaches primarily operate on codon-lev...
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AI semantics for biomedical data integration

bioRxiv Aug 7, 2026
Researchers increasingly need to explore hypotheses that span multimodal data across different scales, organisms, and domains. In practice, this requires connecting knowledge across fragmented databases with incompatible APIs and heterogeneous annota...
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