Manfred Claassen

Molecular Biology · 2009–2021

A researcher in the Science corpus: 7 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 98 citations into it
• Biophysics — 12 citations into it
• Computational Biology & Bioinformatics — 10 citations into it
• Model Architectures — 5 citations into it

Whom Manfred Claassen trusts overall

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

1. Joseph A. DiGiuseppe
2. Martin Ester
3. Christof Angermueller
4. Joshua E. Elias
5. Robert V. Bruggner
6. Michelle D. Tadmor
7. Nima Aghaeepour

Papers from Manfred Claassen's perspective

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

1. ImageNet Large Scale Visual Recognition Challenge
2. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum
3. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
4. Large-scale analysis of the yeast proteome by multidimensional protein identification technology (Nature Biotechnology)
5. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. (Analytical Chemistry)
6. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
7. Mass spectrometry-based proteomics (Nature)
8. CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (Nature Methods)
9. Computational prediction of proteotypic peptides for quantitative proteomics (Nature Biotechnology)
10. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. (Journal of Proteome Research)

Their papers

• False discovery rate estimation for cross-linked peptides identified by mass spectrometry (2012) — 310 citations
• Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009) — 317 citations
• The quantitative proteome of a human cell line (2011) — 786 citations
• Sensitive detection of rare disease-associated cell subsets via representation learning (2016) — 170 citations
• NASH limits anti-tumour surveillance in immunotherapy-treated HCC (2021) — 1,066 citations
• Automated Gleason grading of prostate cancer tissue microarrays via deep learning (2018) — 425 citations
• The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015) — 60 citations