Knut Baumann

Statistics and Probability · 2002–2014

A researcher in the Science corpus: 5 papers; trusts 15 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 23 citations into it
• Artificial Intelligence — 14 citations into it
• Computational Theory and Mathematics — 11 citations into it; #2,134 of 12,182 by trust here, #2,351 by citations
• Molecular Biology — 8 citations into it; #2,204 of 17,773 by trust here, #9,136 by citations
• Materials Chemistry — 5 citations into it

Whom Knut Baumann trusts overall

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

1. Paola Gramatica
2. Alexander Tropsha
3. Trevor J. Hastie
4. Robert Tibshirani
5. Nicola Chirico
6. Steven Lemm
7. Damjan Krstajic
8. Jerome H. Friedman
9. M. Stone
10. Bradley Efron

Who trusts Knut Baumann overall

1. Steven M. Kearnes
2. Sereina Riniker
3. Petr Skoda
4. Ryan L. Murphy
5. Josh Payne
6. Lars Rosenbaum
7. Pedro H. M. Torres
8. Kristina Preuer
9. Mengying Sun
10. Zhirui Liao

Papers from Knut Baumann's perspective

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

1. Beware of q2! (Journal of Molecular Graphics and Modelling)
2. Principles of QSAR models validation: internal and external
3. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
5. Real External Predictivity of QSAR Models: How To Evaluate It? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient (Journal of Chemical Information and Modeling)
6. Cross-validation pitfalls when selecting and assessing regression and classification models (Journal of Cheminformatics)
7. Introduction to machine learning for brain imaging
8. The Elements of Statistical Learning
9. Regression Shrinkage and Selection via the Lasso
10. The Nature of Statistical Learning Theory

Their papers

• Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data (2009) — 375 citations
• Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy (2009) — 94 citations
• A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations (2002) — 83 citations
• Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014) — 142 citations
• inSARa: intuitive single-target (large-scale) SAR interpretation and multi-target cross-reactivity analysis (2014) — 8 citations