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CMOT: Cross-Modality Optimal Transport for multimodal inference
Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular...
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Human inference reflects a normative balance of complexity and accuracy
We must often infer latent properties of the world from noisy and changing observations. Complex, probabilistic approaches to this challenge such as...
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Stress-sensitive inference of task controllability
Estimating the controllability of the environment enables agents to better predict upcoming events and decide when to engage controlled action...
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Active Inference and Abduction
The background target of the research going into the present article is to forge an intellectual alliance between, on the one hand, active inference...
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CoCoA-diff: counterfactual inference for single-cell gene expression analysis
Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease...
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A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm
Bayesian phylogenetics is a computationally challenging inferential problem. Classical methods are based on random-walk Markov chain Monte Carlo...
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Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
BackgroundCurrent methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the...
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Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches...
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siVAE: interpretable deep generative models for single-cell transcriptomes
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are...
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LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model
BackgroundDNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although...
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A generative model of memory construction and consolidation
Episodic memories are (re)constructed, share neural substrates with imagination, combine unique features with schema-based predictions and show...
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Isolating salient variations of interest in single-cell data with contrastiveVI
Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a...
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PyClone-VI: scalable inference of clonal population structures using whole genome data
BackgroundAt diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour...
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TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network...
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DestVI identifies continuums of cell types in spatial transcriptomics data
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis...
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and...
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Improving the performance of Bayesian phylogenetic inference under relaxed clock models
BackgroundBayesian MCMC has become a common approach for phylogenetic inference. But the growing size of molecular sequence data sets has created a...
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LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis
BackgroundDNA methylation is commonly measured using bisulfite sequencing (BS-seq). The quality of a BS-seq library is measured by its bisulfite...
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Flexible modeling of regulatory networks improves transcription factor activity estimation
Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data...
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methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder
BackgroundRecently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative...
