Martin Hemberg

Molecular Biology · 2008–2020

A researcher in the Science corpus: 11 papers; trusts 31 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 303 citations into it; #3,145 of 17,773 by trust here, #1,520 by citations
• Cancer Research — 76 citations into it; #810 of 1,780 by trust here, #1,572 by citations
• Biophysics — 60 citations into it; #895 of 1,196 by trust here, #119 by citations
• Immunology — 46 citations into it; #815 of 2,160 by trust here, #1,439 by citations
• Genetics — 43 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Statistical and Nonlinear Physics — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Martin Hemberg trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. L. V. D. van der Maaten
2. Simon Anders
3. Emma Pierson
4. Peter V. Kharchenko
5. Geoffrey E. Hinton
6. Santo Fortunato
7. Dominic Grün
8. Gordon K. Smyth
9. Yang Liao
10. Charity W. Law

Who trusts Martin Hemberg overall

1. Saket Kumar Choudhary
2. Angelo Duò
3. Yuqi Tan
4. Rahul Satija
5. Aleksandr Ianevski
6. Pavlin Gregor Policar
7. Antoine-Emmanuel Saliba

Papers from Martin Hemberg's perspective

Ordered by how strongly the researchers Martin Hemberg trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Visualizing Data using t-SNE
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)

Their papers

• The Human Cell Atlas (2017) — 1,788 citations
• SC3 - consensus clustering of single-cell RNA-Seq data (2016) — 1,337 citations
• Widespread transcription at neuronal activity-regulated enhancers (2010) — 1,879 citations
• Modelling dropouts for feature selection in scRNASeq experiments (2016) — 38 citations
• Transcriptome-wide noise controls lineage choice in mammalian progenitor cells (2008) — 1,121 citations
• M3Drop: dropout-based feature selection for scRNASeq (2018) — 236 citations
• scmap: projection of single-cell RNA-seq data across data sets (2018) — 634 citations
• Challenges in unsupervised clustering of single-cell RNA-seq data (2019) — 1,049 citations
• souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes (2020) — 380 citations
• Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution (2017) — 636 citations
• Genomic positional conservation identifies topological anchor point (tap)RNAs linked to developmental loci (2016) — 85 citations