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Results for deep generative decoder

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6 papers · ranked by Valyu relevance

01
PPubMed7 Sep 2026
ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions
Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang + 2 more
While protein-RNA interactions are fundamental to post-transcriptional processes, achieving a holistic understanding of their regulatory logic remains challenging. Current computational models often treat binding affinity, interface mapping, and RNA design as isolated tasks, thereby failing to provide a unified…
71relevance
02
BbioRxiv8 Sep 2026
Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces
Yi Ren, Zimei Chen, Hayden Deans, Ying Nian Wu + 1 more
Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the…
63relevance
03
BbioRxiv11 Sep 2026
Reframing enzyme function prediction as conditional generation
William JF Rieger, Sebastian Häussermann, Luca Herrmann, Zecheng Li + 8 more
Enzymes frequently exhibit promiscuous activity beyond their native roles, providing starting-points for new functions. Finding these promiscuous enzymes, especially for non-native chemical transformations, is challenging but highly valuable, as they promise novel, sustainable solutions for chemistry and biotechnology.…
61relevance
04
BbioRxiv11 Sep 2026
scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data
Yuqiao Liu, Siyu Yi, Hengchuang Yin, Wei Ju
Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses…
59relevance
05
PPubMed7 Sep 2026
A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening
Danny Vu, Andrew Kowalczewski, Sarah D. Burnett, Courtney Sakolish + 4 more
Cardiotoxicity remains a major cause of drug attrition and postmarket withdrawal, yet the vast majority of environmental chemicals to which humans may be exposed remain uncharacterized for cardiotoxicity risk. Human induced pluripotent stem cell (hiPSC)-based testing has been proposed to address this gap. Here, we…
57relevance
06
BbioRxiv8 Sep 2026
scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data
Jackie Rao, Muntadher Jihad, Giulia Biffi, Paul D.W. Kirk
Identifying cell types from single-cell RNA sequencing (scRNA-seq) data typically requires several separate and often uninterpretable steps: dimensionality reduction, batch-correction, clustering, marker-gene identification and the discovery of finer-grained structure. Here we introduce scFLAME (single-cell Factor…
56relevance

Results for deep generative decoder

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Reframing enzyme function prediction as conditional generation

scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data

A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Reframing enzyme function prediction as conditional generation

scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data

A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data