1
Predicting transcriptional outcomes of novel multigene perturbations with GEARS
2
Deep learning applications in single-cell genomics and transcriptomics data analysis.
3
Single-Cell Multimodal Prediction via Transformers
4
Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics
5
ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations
6
Abstract 3878: Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager
7
Cell clustering for spatial transcriptomics data with graph neural networks
8
Cross-tissue immune cell analysis reveals tissue-specific features in humans
9
Depth normalization for single-cell genomics count data
10
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
11
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
12
BANKSY: A Spatial Omics Algorithm that Unifies Cell Type Clustering and Tissue Domain Segmentation
13
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
14
Cellpose 2.0: how to train your own model
15
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data
16
Graph Neural Networks for Multimodal Single-Cell Data Integration
17
Single nucleus multi-omics identifies human cortical cell regulatory genome diversity
18
The TissueNet v.3 Database: Protein-protein Interactions in Adult and Embryonic Human Tissue contexts.
19
Deep learning shapes single-cell data analysis
20
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network
21
UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization
22
Recent advances in single-cell sequencing technologies
23
MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images
24
scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning
25
Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data
26
Cell2location maps fine-grained cell types in spatial transcriptomics
27
Zero-preserving imputation of single-cell RNA-seq data
28
scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data
29
MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data
30
Cobolt: integrative analysis of multimodal single-cell sequencing data
31
GNN-based embedding for clustering scRNA-seq data
32
From bulk, single-cell to spatial RNA sequencing
33
Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen
34
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
35
Cell segmentation in imaging-based spatial transcriptomics
36
Intricacies of single-cell multi-omics data integration.
37
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network
38
A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data
39
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
40
Exploring tissue architecture using spatial transcriptomics
41
The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans
42
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
43
Unsupervised spatially embedded deep representation of spatial transcriptomics
44
Single-cell classification using graph convolutional networks
45
Spatial transcriptomics at subspot resolution with BayesSpace
46
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
47
Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells
48
scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation
49
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
50
A survey on deep learning and its applications
51
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
52
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
53
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
54
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
55
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
56
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
57
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
58
The landscape of cell-cell communication through single-cell transcriptomics.
59
scSorter: assigning cells to known cell types according to marker genes
60
coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data
61
MultiMAP: dimensionality reduction and integration of multimodal data
62
Fast and precise single-cell data analysis using a hierarchical autoencoder
63
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
64
SpatialDWLS: accurate deconvolution of spatial transcriptomic data
65
SMILE: Mutual Information Learning for Integration of Single Cell Omics Data
66
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
67
Automated methods for cell type annotation on scRNA-seq data
68
CaMelia: imputation in single-cell methylomes based on local similarities between cells
69
Method of the Year: spatially resolved transcriptomics
70
Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes
71
Unbiased integration of single cell multi-omics data
72
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
73
Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
74
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data
75
BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
76
Biological network analysis with deep learning
77
Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
78
From whole-mount to single-cell spatial assessment of gene expression in 3D
79
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
80
Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions
81
Integrated analysis of multimodal single-cell data
82
Double-Uncertainty Weighted Method for Semi-supervised Learning
83
Likelihood-based deconvolution of bulk gene expression data using single-cell references
84
Joint cell segmentation and cell type annotation for spatial transcriptomics
85
Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network
86
Single-cell transcriptomics in cancer: computational challenges and opportunities
87
Methods for copy number aberration detection from single-cell DNA-sequencing data
88
Museum of spatial transcriptomics
89
Deep learning–based cell composition analysis from tissue expression profiles
90
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
91
Unsupervised manifold alignment for single-cell multi-omics data
92
SCIM: universal single-cell matching with unpaired feature sets
93
scNym: Semi-supervised adversarial neural networks for single cell classification
94
Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology
95
Boundary-assisted Region Proposal Networks for Nucleus Segmentation
96
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues
97
CellMeSH: probabilistic cell-type identification using indexed literature
98
Deep soft K-means clustering with self-training for single-cell RNA sequence data
99
CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments
100
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
101
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
102
Robust decomposition of cell type mixtures in spatial transcriptomics
103
Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration
104
Multiplex digital spatial profiling of proteins and RNA in fixed tissue
105
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation.
106
Jointly defining cell types from multiple single-cell datasets using LIGER
107
Construction of a human cell landscape at single-cell level
108
Embracing the dropouts in single-cell RNA-seq analysis
109
CMF-Impute: an accurate imputation tool for single-cell RNA-seq data
110
Single-cell analysis targeting the proteome
111
Eleven grand challenges in single-cell data science
112
Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks
113
scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
114
Cellpose: a generalist algorithm for cellular segmentation
115
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles
116
Unsupervised topological alignment for single-cell multi-omics integration
117
Deep learning of circulating tumour cells
118
Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression
119
scIGANs: single-cell RNA-seq imputation using generative adversarial networks
120
Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas
121
Faculty Opinions recommendation of Simultaneous epitope and transcriptome measurement in single cells.
122
SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
123
Multi-domain translation between single-cell imaging and sequencing data using autoencoders
124
PyTorch: An Imperative Style, High-Performance Deep Learning Library
125
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
126
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
127
Deep convolutional neural network based medical image classification for disease diagnosis
128
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data
129
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
130
DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data
131
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
132
MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure
133
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
134
EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes
135
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.
136
DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images
137
A molecular cell atlas of the human lung from single cell RNA sequencing
138
Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front
139
Beyond bulk: a review of single cell transcriptomics methodologies and applications.
140
Accurate estimation of cell-type composition from gene expression data
141
SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples
142
Concepts and limitations for learning developmental trajectories from single cell genomics
143
An Introduction to Variational Autoencoders
144
SciBet: a portable and fast single cell type identifier
145
A comparison of automatic cell identification methods for single-cell RNA sequencing data
146
Missing Data in Traffic Estimation: A Variational Autoencoder Imputation Method
147
MixMatch: A Holistic Approach to Semi-Supervised Learning
148
Trajectory-based differential expression analysis for single-cell sequencing data
149
A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
150
scMatch: a single-cell gene expression profile annotation tool using reference datasets
151
Clustering single-cell RNA-seq data with a model-based deep learning approach
152
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+
153
Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning
154
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
155
Interpolation Consistency Training for Semi-Supervised Learning
156
Fast Graph Representation Learning with PyTorch Geometric
157
The Impact of Heterogeneity on Single-Cell Sequencing
158
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
159
SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles
160
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
161
Revolutionizing immunology with single-cell RNA sequencing
162
netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis
163
Supervised classification enables rapid annotation of cell atlases
164
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
165
SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species
166
Imputation of single-cell gene expression with an autoencoder neural network
167
Single-cell proteomics
168
Deep Learning on Graphs: A Survey
169
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
170
Faculty Opinions recommendation of Comprehensive single-cell transcriptional profiling of a multicellular organism.
171
Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology
172
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies
173
Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
174
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
175
scRMD: Imputation for single cell RNA-seq data via robust matrix decomposition
176
Comprehensive Integration of Single-Cell Data
177
Single cell clustering based on cell‐pair differentiability correlation and variance analysis
178
Deep Generative Modeling for Single-cell Transcriptomics
179
Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing Maps
180
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
182
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
183
A Survey on Deep Learning
184
Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization data
185
Experimental Considerations for Single-Cell RNA Sequencing Approaches
186
bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data
187
Single-cell RNA sequencing technologies and bioinformatics pipelines
188
Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis
189
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes
190
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
191
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
192
Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM)
193
Keras: The Python Deep Learning library
194
Cell type transcriptome atlas for the planarian Schmidtea mediterranea
195
Massive single-cell RNA-seq analysis and imputation via deep learning
196
SAVER: Gene expression recovery for single-cell RNA sequencing
197
Large-Scale Unsupervised Deep Representation Learning for Brain Structure
198
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
199
Melissa: Bayesian clustering and imputation of single-cell methylomes
200
Single-cell RNA-seq denoising using a deep count autoencoder
201
Attention U-Net: Learning Where to Look for the Pancreas
202
scmap: projection of single-cell RNA-seq data across data sets
203
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
204
Semisoft clustering of single-cell data
205
SpatialDE: identification of spatially variable genes
206
Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy
207
Bayesian clustering and imputation of single cell methylomes
208
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
209
An accurate and robust imputation method scImpute for single-cell RNA-seq data
210
Bias, robustness and scalability in single-cell differential expression analysis
211
Learning to Make Predictions on Graphs with Autoencoders
212
scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells
213
Mapping the Mouse Cell Atlas by Microwell-Seq
214
MAGAN: Aligning Biological Manifolds
215
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
216
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
217
netSmooth: Network-smoothing based imputation for single cell RNA-seq
218
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
219
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
220
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
221
mixup: Beyond Empirical Risk Minimization
222
Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging
223
Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development
224
Multiplexed quantification of proteins and transcripts in single cells
225
DrImpute: imputing dropout events in single cell RNA sequencing data
226
New skin for the old RNA-Seq ceremony: the age of single-cell multi-omics
227
Inductive Representation Learning on Large Graphs
228
Modeling gene regulation from paired expression and chromatin accessibility data
229
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
230
MIDA: Multiple Imputation Using Denoising Autoencoders
231
Multiple Imputation Using Deep Denoising Autoencoders
232
Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
233
MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics
234
Exponential scaling of single-cell RNA-seq in the past decade
235
Spatial transcriptomics: paving the way for tissue-level systems biology.
237
SPRING: a kinetic interface for visualizing high dimensional single-cell expression data
238
Large-scale simultaneous measurement of epitopes and transcriptomes in single cells
239
MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
240
ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data
241
Deep Learning on Graphs
242
Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes
243
Model-based branching point detection in single-cell data by K-branches clustering
244
Feature Pyramid Networks for Object Detection
245
DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation
246
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
247
A Single-Cell Transcriptome Atlas of the Human Pancreas
248
In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus
249
Semi-Supervised Classification with Graph Convolutional Networks
250
Power Analysis of Single Cell RNA-Sequencing Experiments
251
SC3 - consensus clustering of single-cell RNA-Seq data
252
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
253
Massively parallel digital transcriptional profiling of single cells
254
MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ
255
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
256
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
257
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
258
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
259
Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
260
Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas
261
Oufti: an integrated software package for high‐accuracy, high‐throughput quantitative microscopy analysis
262
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
263
Deep Residual Learning for Image Recognition
264
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
265
An Introduction to Convolutional Neural Networks
266
Unsupervised Deep Embedding for Clustering Analysis
267
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
268
The first five years of single-cell cancer genomics and beyond
269
Automatic single cell segmentation on highly multiplexed tissue images
270
A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data
271
Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation.
272
Deep Convolutional Networks on Graph-Structured Data
273
Identification of cell types from single-cell transcriptomes using a novel clustering method
274
You Only Look Once: Unified, Real-Time Object Detection
275
Domain-Adversarial Training of Neural Networks
276
Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
277
The technology and biology of single-cell RNA sequencing.
278
Advances and applications of single-cell sequencing technologies.
279
U-Net: Convolutional Networks for Biomedical Image Segmentation
280
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
281
Cell segmentation and classification by hierarchical supervised shape ranking
282
Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics
283
Computational and analytical challenges in single-cell transcriptomics
284
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
285
Spatially resolved transcriptomics and beyond
286
Fully convolutional networks for semantic segmentation
287
Single-cell sequencing technologies: current and future.
288
Single-Cell Genome-Wide Bisulfite Sequencing for Assessing Epigenetic Heterogeneity
289
Bayesian approach to single-cell differential expression analysis
290
Single-cell in situ RNA profiling by sequential hybridization
291
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
292
Single-cell technologies for monitoring immune systems
293
Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing
294
Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue
295
On the difficulty of training recurrent neural networks
296
The accessible chromatin landscape of the human genome
297
A brief introduction to OpenCV
298
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.
299
Ilastik: Interactive learning and segmentation toolkit
300
Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software
301
Phenomics: the next challenge
302
GREAT improves functional interpretation of cis-regulatory regions
303
Multilayer perceptron and neural networks
304
mRNA-Seq whole-transcriptome analysis of a single cell
305
Exploring Network Structure, Dynamics, and Function using NetworkX
306
Fast unfolding of community hierarchies in large networks
307
How to infer gene networks from expression profiles
308
Perceptions of epigenetics
309
How to infer gene networks from expression profiles
310
The origins and the future of microfluidics
311
Single-Cell Microbiology: Tools, Technologies, and Applications
312
Proteomics: the first decade and beyond
313
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
314
Principal component analysis for clustering gene expression data
315
Dynamic pattern formation in a vesicle-generating microfluidic device.
316
Convolutional networks for images, speech, and time series
317
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
318
Long Short-Term Memory
319
Analysis of gene expression in single live neurons.
320
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
321
Learning representations by back-propagating errors
322
Learning internal representations by error propagation
323
Chromatin structure: a repeating unit of histones and DNA.
325
Clustering Using a Similarity Measure Based on Shared Near Neighbors
326
Hierarchical clustering schemes
327
The Demonstration of Pneumococcal Antigen in Tissues by the Use of Fluorescent Antibody
328
DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis
329
Ingileif B Hallgŕımsdóttir
330
CA2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection
331
A sandbox for prediction and integration of DNA, RNA, and protein data in single cells
332
A Likelihood-based Deconvolution of Bulk Gene Expression Data Using Single-cell References
333
OUP accepted manuscript
334
Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes
335
AUTO-ENCODING VARIATIONAL BAYES
336
ACTINN: automated identification of cell types in single cell RNA sequencing
339
Single-Cell Tagged Reverse Transcription (STRT-Seq).
340
Recurrent Neural Networks
341
Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC-Seq and RNA-Seq.
342
GENERATIVE ADVERSARIAL NETS
343
Simlr: A tool for large-scale genomic analyses by multi-kernel learning
344
Missing Data Imputation in the Electronic Health Record Using Deeply Learned Autoencoders
345
RNA Imaging with Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH).
347
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method
348
Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics
349
Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
350
DNA Methylation and Its Basic Function
351
Statistical Applications in Genetics and Molecular Biology Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
352
A UNIFIED SEGMENTATION METHOD FOR DETECTING SUBCELLULAR COMPARTMENTS IN IMMUNOFLUORESCENTLY LABELED TISSUE IMAGES
353
The Graph Neural Network Model
354
Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images
356
Self-organized formation of topologically correct feature maps
357
Details of the Adjusted Rand index and Clustering algorithms Supplement to the paper “ An empirical study on Principal Component Analysis for clustering gene expression data ” ( to appear in Bioinformatics )
359
Chromatin structure and transcription.
360
A logical calculus of the ideas immanent in nervous activity
361
Representative in Vitro cDNA Amplification From Individual Hemopoietic Cells and Colonies
362
Some methods for classification and analysis of multivariate observations
363
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning
364
Hek293t and ccrf-cem cell line mixture data
365
com/spatial-gene-expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_ Posterior
366
Mouse posterior brain 10x visium data
367
181] 10x genomics cite-seq
368
Mouse olfactory bulb data
