Kunal Roy

Computational Theory and Mathematics · 2008–2017

A researcher in the Science corpus: 9 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 57 citations into it
• Materials Chemistry — 25 citations into it
• Molecular Biology — 24 citations into it
• Statistics and Probability — 20 citations into it
• Analytical Chemistry — 17 citations into it
• Food Science — 5 citations into it

Whom Kunal Roy trusts overall

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

1. Alexander Tropsha
2. Paola Gramatica
3. Viviana Consonni
4. Douglas M. Hawkins
5. Nicola Chirico
6. I. Mitra
7. Probir Kumar Ojha
8. Kuo-Chen Chou
9. Davide Ballabio
10. Partha Roy

Who trusts Kunal Roy overall

1. Hakime Öztürk
2. B. Sepehri
3. Paola Gramatica
4. Elif Ozkirimli Olmez

Papers from Kunal Roy's perspective

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

1. The Problem of Overfitting
2. Beware of q2! (Journal of Molecular Graphics and Modelling)
3. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
4. A Mathematical Theory of Communication
5. Neural Network Studies
6. Density Estimation for Statistics and Data Analysis
7. Principles of QSAR models validation: internal and external
8. Multi‐ and Megavariate Data Analysis. Principles and Applications, I. Eriksson, E. Johansson, N. Kettaneh‐Wold and S. Wold, Umetrics Academy, Umeå, 2001, ISBN 91‐973730‐1‐X, 533pp.
9. The Gene Ontology Consortium
10. Introduction to Multivariate Statistical Analysis in Chemometrics

Their papers

• Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data (2013) — 443 citations
• How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models (2017) — 135 citations
• Be aware of error measures. Further studies on validation of predictive QSAR models (2016) — 637 citations
• On a simple approach for determining applicability domain of QSAR models (2015) — 683 citations
• Introduction of rm2(rank) metric incorporating rank-order predictions as an additional tool for validation of QSAR/QSPR models (2012) — 68 citations
• Comparative Studies on Some Metrics for External Validation of QSPR Models (2012) — 432 citations
• Further exploring rm2 metrics for validation of QSPR models (2011) — 514 citations
• On further application of r  m2 as a metric for validation of QSAR models (2009) — 91 citations
• Exploring the impact of size of training sets for the development of predictive QSAR models (2008) — 292 citations