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PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of...
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Bayesian compositional regression with microbiome features via variational inference
The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other...
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VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder
MotivationAccurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing....
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The covariance environment defines cellular niches for spatial inference
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods...
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CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data
Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory...
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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
BackgroundNumerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict...
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Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy
Tissue morphogenesis results from a tight interplay between gene expression, biochemical signaling and mechanics. Although sequencing methods allow...
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Canonical neural networks perform active inference
This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost...
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A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
BackgroundThe advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in...
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scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics
Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is...
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Efficient stabilization of imprecise statistical inference through conditional belief updating
Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference...
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Improved genomic prediction using machine learning with Variational Bayesian sparsity
BackgroundGenomic prediction has become a powerful modelling tool for assessing line performance in plant and livestock breeding programmes. Among...
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Inference and model determination for temperature-driven non-linear ecological models
This paper is concerned with a contemporary Bayesian approach to the effect of temperature on developmental rates. We develop statistical methods...
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Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data
BackgroundDeep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA...
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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance
BackgroundEnormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between...
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ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization
Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by...
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Identifying behavioral structure from deep variational embeddings of animal motion
Quantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose...
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PCA outperforms popular hidden variable inference methods for molecular QTL map**
BackgroundEstimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular...
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BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin
We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF...
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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets...
