Giuseppe Jurman

Molecular Biology · 2009–2023

A researcher in the Science corpus: 10 papers; trusts 58 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 64 citations into it; #15,000 of 17,773 by trust here, #5,137 by citations
• Statistics and Probability — 61 citations into it
• Artificial Intelligence — 54 citations into it; #9,522 of 35,345 by trust here, #12,580 by citations
• Evaluation & Benchmarks — 23 citations into it
• Information Systems — 19 citations into it; #2,046 of 8,771 by trust here, #3,402 by citations
• Management Science and Operations Research — 17 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #20,106 of 21,575 by trust here, #16,367 by citations
• Global and Planetary Change — 9 citations into it

Whom Giuseppe Jurman trusts overall

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

1. Davide Chicco
2. Jaka Demsar
3. Jan Gorodkin
4. Andrew P. Bradley
5. Steve Hranilovic
6. Eamonn J. Keogh
7. David Martin Ward Powers
8. James A. Hanley
9. Salvador Garcia
10. Jin Huang

Who trusts Giuseppe Jurman overall

1. Davide Chicco
2. Oguzhan Gencoglu
3. Mugahed A. Al-antari
4. Jafar Tanha
5. Margherita Grandini
6. Carter J. Wilson
7. Matthijs J. Warrens
8. Bingzhi Chen
9. Jing Jin
10. Mohammed A. Al-masni

Papers from Giuseppe Jurman's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Going deeper with convolutions
7. Learning Multiple Layers of Features from Tiny Images
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Long Short-Term Memory
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• A promoter-level mammalian expression atlas (2014) — 1,941 citations
• Repeatability of published microarray gene expression analyses (2009) — 557 citations
• The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation (2021) — 793 citations
• The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models (2010) — 549 citations
• The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification (2023) — 542 citations
• The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment (2021) — 331 citations
• The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation (2021) — 4,647 citations
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) — 5,423 citations
• Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone (2020) — 532 citations
• A Comparison of MCC and CEN Error Measures in Multi-Class Prediction (2010) — 381 citations