Aleksandr Ianevski
Molecular Biology · 2017–2022
A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 26 citations into it
- Cancer Research — 11 citations into it
- Immunology — 8 citations into it
- Biophysics — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Aleksandr Ianevski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Yu. Kiselev
- Hannah A. Pliner
- Luke Zappia
- Bhagwan Yadav
- Josh Tenenbaum
- Martin Ester
- Jian Tang
- Heng Li
- Tallulah S. Andrews
- Tero Aittokallio
Who trusts Aleksandr Ianevski overall
Papers from Aleksandr Ianevski's perspective
Ordered by how strongly the researchers Aleksandr Ianevski trusts endorse each paper. Same corpus, a different order for every researcher.
- A global geometric framework for nonlinear dimensionality reduction.
- A survey of human brain transcriptome diversity at the single cell level (Proceedings of the National Academy of Sciences of the United States of America)
- The problem of synergism and antagonism of combined drugs.
- Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
- Comprehensive integration of single-cell data (bioRxiv)
- A Single-Cell Transcriptome Atlas of the Human Pancreas (Cell Systems)
- Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (Nature Biotechnology)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Their papers
- SynergyFinder: a web application for analyzing drug combination dose–response matrix data (2017) — 507 citations
- SynergyFinder 2.0: visual analytics of multi-drug combination synergies (2020) — 728 citations
- Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022) — 606 citations