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Multiomics-Based Tensor Decomposition for Characterizing Breast Cancer Heterogeneity
Breast cancer is heterogeneous and consists of intrinsic components with various alterations. Combining multiple genomic sources to identify the...
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Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender
Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing...
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Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps
Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at...
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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional...
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The technological landscape and applications of single-cell multi-omics
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics...
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A hierarchical machine learning framework for the analysis of large scale animal movement data
BackgroundIn recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data...
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Representing and extracting knowledge from single-cell data
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated...
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3DFlex: determining structure and motion of flexible proteins from cryo-EM
Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to...
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Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers
Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various...
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Statistical and Computational Methods for Microbial Strain Analysis
Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced “too many” recombination events and can be...
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Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview
The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently...
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MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells
Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of...
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Modeling intercellular communication in tissues using spatial graphs of cells
Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor–ligand signaling and...
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Super-resolved spatial transcriptomics by deep data fusion
Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression...
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MultiVI: deep generative model for the integration of multimodal data
Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular...
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MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation
Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational...
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VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants
Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is...
