Sohrab P. Shah

Molecular Biology · 2009–2021

A researcher in the Science corpus: 18 papers; trusts 23 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 270 citations into it; #6,027 of 17,773 by trust here, #1,300 by citations
• Artificial Intelligence — 61 citations into it; #17,011 of 35,345 by trust here, #15,236 by citations
• Biophysics — 55 citations into it; #369 of 1,196 by trust here, #471 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Immunology — 24 citations into it
• Model Architectures — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Sohrab P. Shah trusts overall

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

1. L. V. D. van der Maaten
2. Geoffrey E. Hinton
3. Danilo Jimenez Rezende
4. Yoshua Bengio
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Yoav Benjamini
8. Kaiming He
9. Yann LeCun
10. Sepp Hochreiter

Who trusts Sohrab P. Shah overall

1. Charles Swanton
2. Hans Clevers
3. Romain Lopez
4. Fatih Ozsolak
5. Nicholas McGranahan
6. Malte D. Luecken

Papers from Sohrab P. Shah's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017) — 339 citations
• ARID1A mutations in endometriosis-associated ovarian carcinomas. (2010) — 1,661 citations
• deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data (2011) — 562 citations
• Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution (2009) — 1,143 citations
• The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups (2012) — 5,719 citations
• The clonal and mutational evolution spectrum of primary triple-negative breast cancers (2012) — 1,975 citations
• Eleven grand challenges in single-cell data science (2020) — 1,198 citations
• PyClone: statistical inference of clonal population structure in cancer (2014) — 958 citations
• Somatic mutation of EZH2 (Y641) in Follicular and Diffuse Large B-cell Lymphomas of Germinal Center Origin (2010) — 920 citations
• Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling (2019) — 317 citations
• Harnessing multimodal data integration to advance precision oncology (2021) — 563 citations
• The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes (2016) — 1,612 citations
• Hereditary Diffuse Gastric Cancer Syndrome: CDH1 Mutations and Beyond. (2015) — 666 citations
• Dynamics of genomic clones in breast cancer patient xenografts at single cell resolution (2014) — 608 citations
• TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data (2014) — 367 citations
• MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers (2011) — 612 citations
• Tumor-Associated Macrophages and Survival in Classic Hodgkin's Lymphoma (2010) — 1,222 citations
• Mutation of FOXL2 in granulosa-cell tumors of the ovary. (2009) — 734 citations