Alexander Lachmann
Molecular Biology · 2010–2021
A researcher in the Science corpus: 7 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 114 citations into it
- Genetics — 19 citations into it
Whom Alexander Lachmann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- L. V. D. van der Maaten
- Sanjoy Dasgupta
- Sean Davis
- Leena Salmela
- Ralph E. Steuer
- Geoffrey E. Hinton
- Chris Stark
- Anupam Gupta
- Paul S. Meltzer
- Eric Rivals
Who trusts Alexander Lachmann overall
Papers from Alexander Lachmann's perspective
Ordered by how strongly the researchers Alexander Lachmann trusts endorse each paper. Same corpus, a different order for every researcher.
- Et al
- Visualizing Data using t-SNE
- From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
- The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity (Nature)
- ImageNet classification with deep convolutional neural networks
- Molecular signatures database (MSigDB) 3.0
- Gene Expression Omnibus: NCBI gene expression and hybridization array data repository (Nucleic Acids Res.)
- Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
- ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments (Bioinform.)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Their papers
- Massive mining of publicly available RNA-seq data from human and mouse (2017) — 666 citations
- Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016) — 9,421 citations
- ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments (2010) — 982 citations
- Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016) — 684 citations
- The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations (2017) — 380 citations
- Gene Set Knowledge Discovery with Enrichr (2021) — 2,914 citations
- ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information (2016) — 351 citations