AI x Bio Discovery
@aixbiobot.bsky.social
Automated discovery of AI x Bio preprint papers.
De Novo Design of SIK3 Inhibitors via Feedback-Driven Fine-Tuning of Seq2Seq-VAE [new]
De novo design... uses active learning to refine a Seq2Seq-VAE, generating potential SIK3 inhibitors without extensive data.
De novo design... uses active learning to refine a Seq2Seq-VAE, generating potential SIK3 inhibitors without extensive data.
November 11, 2025 at 4:04 AM
De Novo Design of SIK3 Inhibitors via Feedback-Driven Fine-Tuning of Seq2Seq-VAE [new]
De novo design... uses active learning to refine a Seq2Seq-VAE, generating potential SIK3 inhibitors without extensive data.
De novo design... uses active learning to refine a Seq2Seq-VAE, generating potential SIK3 inhibitors without extensive data.
Entangled Schrödinger Bridge Matching][new]
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
November 11, 2025 at 4:00 AM
Entangled Schrödinger Bridge Matching][new]
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
November 11, 2025 at 3:41 AM
Billion-Scale Deciphering of Human Gene Regulatory Grammar [new]
Million-scale data links degenerate seqs to gene expr in human cells. Reveals TF occupancy, coop regulation, & generalizable seq rules.
Million-scale data links degenerate seqs to gene expr in human cells. Reveals TF occupancy, coop regulation, & generalizable seq rules.
November 11, 2025 at 3:38 AM
Billion-Scale Deciphering of Human Gene Regulatory Grammar [new]
Million-scale data links degenerate seqs to gene expr in human cells. Reveals TF occupancy, coop regulation, & generalizable seq rules.
Million-scale data links degenerate seqs to gene expr in human cells. Reveals TF occupancy, coop regulation, & generalizable seq rules.
Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm [new]
Integrates physics with ML to model biomolecular systems, addressing the "closure problem" for kinetics, rare events, and free-energy estimation.
Integrates physics with ML to model biomolecular systems, addressing the "closure problem" for kinetics, rare events, and free-energy estimation.
November 11, 2025 at 3:36 AM
Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm [new]
Integrates physics with ML to model biomolecular systems, addressing the "closure problem" for kinetics, rare events, and free-energy estimation.
Integrates physics with ML to model biomolecular systems, addressing the "closure problem" for kinetics, rare events, and free-energy estimation.
Dual-Pathway Fusion of EHRs and Knowledge Graphs for Predicting Unseen Drug-Drug Interactions [new]
Predicts DDIs using knowledge graphs & EHRs via teacher/student model, generalizing to new drugs with interpretable mechanisms.
Predicts DDIs using knowledge graphs & EHRs via teacher/student model, generalizing to new drugs with interpretable mechanisms.
November 11, 2025 at 3:34 AM
Dual-Pathway Fusion of EHRs and Knowledge Graphs for Predicting Unseen Drug-Drug Interactions [new]
Predicts DDIs using knowledge graphs & EHRs via teacher/student model, generalizing to new drugs with interpretable mechanisms.
Predicts DDIs using knowledge graphs & EHRs via teacher/student model, generalizing to new drugs with interpretable mechanisms.
Integrating Epigenetic and Phenotypic Features for Biological Age Estimation in Cancer Patients via Multimodal Learning [new]
Combines epigenetic and phenotypic data via multimodal learning to estimate biological age in cancer patients.
Combines epigenetic and phenotypic data via multimodal learning to estimate biological age in cancer patients.
November 11, 2025 at 3:30 AM
Integrating Epigenetic and Phenotypic Features for Biological Age Estimation in Cancer Patients via Multimodal Learning [new]
Combines epigenetic and phenotypic data via multimodal learning to estimate biological age in cancer patients.
Combines epigenetic and phenotypic data via multimodal learning to estimate biological age in cancer patients.
A Diffusion Model to Shrink Proteins While Maintaining Their Function [new]
Develops a diffusion model to shrink proteins by learning to delete sequence letters, creating shorter, functional variants resembling natural proteins.
Develops a diffusion model to shrink proteins by learning to delete sequence letters, creating shorter, functional variants resembling natural proteins.
November 11, 2025 at 3:28 AM
A Diffusion Model to Shrink Proteins While Maintaining Their Function [new]
Develops a diffusion model to shrink proteins by learning to delete sequence letters, creating shorter, functional variants resembling natural proteins.
Develops a diffusion model to shrink proteins by learning to delete sequence letters, creating shorter, functional variants resembling natural proteins.
Assessing the feasibility of machine learning for ancient DNA age prediction: limitations and insights [new]
aDNA damage-based ML age prediction fails. Context/decay models may improve it.
aDNA damage-based ML age prediction fails. Context/decay models may improve it.
November 11, 2025 at 3:04 AM
Assessing the feasibility of machine learning for ancient DNA age prediction: limitations and insights [new]
aDNA damage-based ML age prediction fails. Context/decay models may improve it.
aDNA damage-based ML age prediction fails. Context/decay models may improve it.
Can Lightweight LLM Agents Improve Spatial TranscriptomicsAnnotation? [new]
Lightweight LLMs annotated spatial transcriptomics data by integrating rule-based heuristics and multi-role reasoning within an agentic framework.
Lightweight LLMs annotated spatial transcriptomics data by integrating rule-based heuristics and multi-role reasoning within an agentic framework.
November 10, 2025 at 10:54 PM
Can Lightweight LLM Agents Improve Spatial TranscriptomicsAnnotation? [new]
Lightweight LLMs annotated spatial transcriptomics data by integrating rule-based heuristics and multi-role reasoning within an agentic framework.
Lightweight LLMs annotated spatial transcriptomics data by integrating rule-based heuristics and multi-role reasoning within an agentic framework.
Lightweight open-source fine-tuning of SAM2 enables domain-specific microscopy segmentation [new]
Domain microscopy seg. w/ SAM2 fine-tuning & informed post-processing.
Domain microscopy seg. w/ SAM2 fine-tuning & informed post-processing.
November 10, 2025 at 10:32 PM
Lightweight open-source fine-tuning of SAM2 enables domain-specific microscopy segmentation [new]
Domain microscopy seg. w/ SAM2 fine-tuning & informed post-processing.
Domain microscopy seg. w/ SAM2 fine-tuning & informed post-processing.
Enhancing Intra-Continental Biogeographical Ancestry Prediction Through a Machine Learning Marker Selection Method [new]
ML refines ancestry prediction via marker selection, boosting intra-continental accuracy.
ML refines ancestry prediction via marker selection, boosting intra-continental accuracy.
November 10, 2025 at 9:49 PM
Enhancing Intra-Continental Biogeographical Ancestry Prediction Through a Machine Learning Marker Selection Method [new]
ML refines ancestry prediction via marker selection, boosting intra-continental accuracy.
ML refines ancestry prediction via marker selection, boosting intra-continental accuracy.
Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM [new]
Data-augmented DL enables zero-shot PTM prediction, improving site ID & localization in proteomics.
Data-augmented DL enables zero-shot PTM prediction, improving site ID & localization in proteomics.
November 10, 2025 at 8:15 PM
Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM [new]
Data-augmented DL enables zero-shot PTM prediction, improving site ID & localization in proteomics.
Data-augmented DL enables zero-shot PTM prediction, improving site ID & localization in proteomics.
SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics [new]
Spat. transcriptomics: context, boundaries, neighbs, & genes.
Spat. transcriptomics: context, boundaries, neighbs, & genes.
November 10, 2025 at 8:13 PM
SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics [new]
Spat. transcriptomics: context, boundaries, neighbs, & genes.
Spat. transcriptomics: context, boundaries, neighbs, & genes.
MetaboliteChat: A Unified Multimodal Large Language Model for Interactive Metabolite Analysis and Functional Insights [new]
Multimodal LLM integrates metabolite graphs, images, and language for interactive analysis/prediction.
Multimodal LLM integrates metabolite graphs, images, and language for interactive analysis/prediction.
November 10, 2025 at 8:09 PM
MetaboliteChat: A Unified Multimodal Large Language Model for Interactive Metabolite Analysis and Functional Insights [new]
Multimodal LLM integrates metabolite graphs, images, and language for interactive analysis/prediction.
Multimodal LLM integrates metabolite graphs, images, and language for interactive analysis/prediction.
Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes [new]
LLMs integrate omics via Q\&A pairs, emulating self-supervised learning.
LLMs integrate omics via Q\&A pairs, emulating self-supervised learning.
November 10, 2025 at 8:07 PM
Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes [new]
LLMs integrate omics via Q\&A pairs, emulating self-supervised learning.
LLMs integrate omics via Q\&A pairs, emulating self-supervised learning.
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation [new]
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
November 10, 2025 at 7:03 PM
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation [new]
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
CONCERT predicts niche-aware perturbation responses in spatial transcriptomics [new]
CONCERT: Predicts niche-aware perturbation responses in spatial transcriptomics using spatial kernels within a generative model.
CONCERT: Predicts niche-aware perturbation responses in spatial transcriptomics using spatial kernels within a generative model.
November 10, 2025 at 6:20 PM
CONCERT predicts niche-aware perturbation responses in spatial transcriptomics [new]
CONCERT: Predicts niche-aware perturbation responses in spatial transcriptomics using spatial kernels within a generative model.
CONCERT: Predicts niche-aware perturbation responses in spatial transcriptomics using spatial kernels within a generative model.
HEIMDALL: A Modular Framework for Tokenization in Single-Cell Foundation Models [new]
HEIMDALL:
...evaluates tokenization strategies in single-cell foundation models by modularizing components for fine-grained control and analysis.
HEIMDALL:
...evaluates tokenization strategies in single-cell foundation models by modularizing components for fine-grained control and analysis.
November 10, 2025 at 6:03 PM
HEIMDALL: A Modular Framework for Tokenization in Single-Cell Foundation Models [new]
HEIMDALL:
...evaluates tokenization strategies in single-cell foundation models by modularizing components for fine-grained control and analysis.
HEIMDALL:
...evaluates tokenization strategies in single-cell foundation models by modularizing components for fine-grained control and analysis.
An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings [new]
Identifies universal aging signatures in cell data by disentangling aging-related gene expression from other biological variations.
Identifies universal aging signatures in cell data by disentangling aging-related gene expression from other biological variations.
November 10, 2025 at 6:00 PM
An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings [new]
Identifies universal aging signatures in cell data by disentangling aging-related gene expression from other biological variations.
Identifies universal aging signatures in cell data by disentangling aging-related gene expression from other biological variations.
Dynamics-enhanced Molecular Property Prediction Guided by Deep Learning [new]
Leverages molecular dynamics simulations and deep learning to enhance property prediction by considering molecular dynamics.
Leverages molecular dynamics simulations and deep learning to enhance property prediction by considering molecular dynamics.
November 10, 2025 at 5:08 PM
Dynamics-enhanced Molecular Property Prediction Guided by Deep Learning [new]
Leverages molecular dynamics simulations and deep learning to enhance property prediction by considering molecular dynamics.
Leverages molecular dynamics simulations and deep learning to enhance property prediction by considering molecular dynamics.
PyrMol: A Knowledge-Structured Pyramid Graph Framework forGeneralizable Molecular Property Prediction [new]
Hierarchical GNNs boost molecule property prediction generalization by integrating multi-level chem knowledge.
Hierarchical GNNs boost molecule property prediction generalization by integrating multi-level chem knowledge.
November 10, 2025 at 3:02 PM
PyrMol: A Knowledge-Structured Pyramid Graph Framework forGeneralizable Molecular Property Prediction [new]
Hierarchical GNNs boost molecule property prediction generalization by integrating multi-level chem knowledge.
Hierarchical GNNs boost molecule property prediction generalization by integrating multi-level chem knowledge.
Prognosis prediction using autophagy gene expression in osteosarcoma [new]
Autophagy gene data informs a classifier (Auto-RS) for individualized osteosarcoma risk, capturing metastatic trajectories and revealing drug vulnerabilities.
Autophagy gene data informs a classifier (Auto-RS) for individualized osteosarcoma risk, capturing metastatic trajectories and revealing drug vulnerabilities.
November 10, 2025 at 1:39 PM
Prognosis prediction using autophagy gene expression in osteosarcoma [new]
Autophagy gene data informs a classifier (Auto-RS) for individualized osteosarcoma risk, capturing metastatic trajectories and revealing drug vulnerabilities.
Autophagy gene data informs a classifier (Auto-RS) for individualized osteosarcoma risk, capturing metastatic trajectories and revealing drug vulnerabilities.
Challenges in predicting chromatin accessibility differences between species [new]
Models predict chromatin accessibility across species but struggle with quantitative differences in orthologous regions, highlighting limitations.
Models predict chromatin accessibility across species but struggle with quantitative differences in orthologous regions, highlighting limitations.
November 10, 2025 at 12:52 PM
Challenges in predicting chromatin accessibility differences between species [new]
Models predict chromatin accessibility across species but struggle with quantitative differences in orthologous regions, highlighting limitations.
Models predict chromatin accessibility across species but struggle with quantitative differences in orthologous regions, highlighting limitations.
Inferring the regulation dynamics of oscillatorynetworks from scRNA-seq data [new]
Inferred cell cycle positions enhance gene regulatory network inference, especially in early progenitor cells, by improving on temporal ordering.
Inferred cell cycle positions enhance gene regulatory network inference, especially in early progenitor cells, by improving on temporal ordering.
November 10, 2025 at 11:49 AM
Inferring the regulation dynamics of oscillatorynetworks from scRNA-seq data [new]
Inferred cell cycle positions enhance gene regulatory network inference, especially in early progenitor cells, by improving on temporal ordering.
Inferred cell cycle positions enhance gene regulatory network inference, especially in early progenitor cells, by improving on temporal ordering.