#gene-expression News & Analysis

7 articles tagged with #gene-expression. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · Jun 236/10

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Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics

Researchers propose CAMMST, a Masked Autoencoder framework that predicts gene expression from histology images by leveraging small amounts of spatial transcriptomics data as genetic anchors. The method combines visual and genetic modalities through contrastive learning, achieving superior performance with minimal transcriptomic coverage and addressing the cost limitations of spatial transcriptomics profiling.

AINeutralarXiv – CS AI · Jun 96/10

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SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network

Researchers introduce SNR-ST-Mix, a data augmentation framework designed specifically for spatial transcriptomics that uses geometry-aware and expression-aware mixing to improve deep neural network performance. The method constrains data interpolation to k-nearest spatial neighbors and weights coefficients by expression similarity, enabling more biologically plausible synthetic training samples that enhance prediction accuracy without architectural changes.

AINeutralarXiv – CS AI · Jun 26/10

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Plausibility Is Not Prediction: Contrastive Evidence for LLM-Based Cellular Perturbation Reasoning

Researchers demonstrate that large language models fail to accurately predict gene expression changes in cellular perturbation experiments despite producing biologically plausible explanations. They introduce CORE, a contrastive learning method that significantly improves prediction accuracy by organizing evidence from related perturbations rather than evaluating them in isolation.

AINeutralarXiv – CS AI · Jun 26/10

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GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

Researchers introduce GC-MoE, a machine learning framework that predicts individual cell gene expression from histopathology images and spatial data, addressing limitations of existing methods that only work at the spot level. The approach combines cell-type-specific expert models with genomic guidance to capture cellular expression variability more accurately than current baselines.

AINeutralarXiv – CS AI · May 116/10

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OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

Researchers introduce OmicsLM, a multimodal large language model that interprets transcriptomic data by combining quantitative gene expression profiles with natural language processing. Trained on 5.5 million examples across 70 task types, the model outperforms specialized omics tools and general LLMs on language-guided biological reasoning tasks, advancing AI applications in genomic research.

AINeutralarXiv – CS AI · May 115/10

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Switching-time bioprocess control with pulse-width-modulated optogenetics

Researchers propose using pulse-width modulation (PWM) with reinforcement learning to optimize optogenetic bioprocess control, enabling precise gene expression tuning through light-based switching rather than intensity adjustment. This approach addresses the limitation of steep dose-response curves in biotechnology by alternating light ON/OFF states within control periods, improving controllability and production efficiency in protein synthesis and metabolic regulation.

AIBullisharXiv – CS AI · Feb 276/104

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Multi-Dimensional Spectral Geometry of Biological Knowledge in Single-Cell Transformer Representations

Researchers decoded the internal representations of scGPT, a single-cell foundation model, revealing it organizes genes into interpretable biological coordinate systems rather than opaque features. The model encodes cellular organization patterns including protein localization, interaction networks, and regulatory relationships across its transformer layers.