Alexey I. Nesvizhskii

Molecular Biology · 2002–2022

A researcher in the Science corpus: 25 papers; trusts 80 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 387 citations into it; #562 of 17,773 by trust here, #1,262 by citations
• Spectroscopy — 202 citations into it; #30 of 1,674 by trust here, #52 by citations
• Genetics — 27 citations into it
• Biophysics — 9 citations into it
• Statistics and Probability — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Alexey I. Nesvizhskii trusts overall

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

1. R. Aebersold
2. Joshua E. Elias
3. John D. Storey
4. Nicholas T. Ingolia
5. R. Craig
6. Bradley Efron
7. Eric W. Deutsch
8. Marcus Bantscheff
9. Robert Tibshirani
10. Steven P. Gygi

Who trusts Alexey I. Nesvizhskii overall

1. Eric W. Deutsch
2. Fengchao Yu
3. David D. Shteynberg
4. Hyungwon Choi
5. Jimmy K. Eng
6. Chih-Chiang Tsou
7. Sangtae Kim
8. B. Searle
9. Andy T. Kong
10. Dmitry Avtonomov

Papers from Alexey I. Nesvizhskii's perspective

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

1. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. (Analytical Chemistry)
2. Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* (Molecular & Cellular Proteomics)
3. Controlling the false discovery rate: a practical and powerful approach to multiple testing
4. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. (Analytical Chemistry)
5. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry (Nature Methods)
6. The Need for Guidelines in Publication of Peptide and Protein Identification Data (Molecular & Cellular Proteomics)
7. Open mass spectrometry search algorithm. (Journal of Proteome Research)
8. Basic local alignment search tool.
9. A common open representation of mass spectrometry data and its application to proteomics research (Nature Biotechnology)
10. Mass spectrometry-based proteomics (Nature)

Their papers

• The PeptideAtlas project (2005) — 812 citations
• A statistical model for identifying proteins by tandem mass spectrometry. (2003) — 4,826 citations
• Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. (2002) — 4,912 citations
• The Need for Guidelines in Publication of Peptide and Protein Identification Data (2004) — 476 citations
• A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010) — 525 citations
• Analysis and validation of proteomic data generated by tandem mass spectrometry (2007) — 678 citations
• Interpretation of Shotgun Proteomic Data (2005) — 612 citations
• A Global Protein Kinase and Phosphatase Interaction Network in Yeast (2010) — 716 citations
• SAINTexpress: improvements and additional features in Significance Analysis of Interactome software (2013) — 612 citations
• iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011) — 533 citations
• The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data (2013) — 1,597 citations
• SAINT: Probabilistic Scoring of Affinity Purification - Mass Spectrometry Data (2010) — 740 citations
• MSFragger: ultrafast and comprehensive peptide identification in shotgun proteomics (2017) — 1,509 citations
• BioContainers: an open-source and community-driven framework for software standardization (2017) — 759 citations
• DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015) — 596 citations
• A multi-center study benchmarks software tools for label-free proteome quantification (2016) — 316 citations
• Philosopher: a versatile toolkit for shotgun proteomics data analysis (2020) — 515 citations
• Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008) — 380 citations
• The Landscape of Circular RNA in Cancer (2019) — 1,367 citations
• dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts (2022) — 327 citations