Furlanello, Cesare

Gut microbiota profiling of pediatric nonalcoholic fatty liver disease and obese patients unveiled by an integrated meta-omics-based approach

Del Chierico, F., Nobili, V., Vernocchi, P., Russo, A., De Stefanis, C., Gnani, D., Furlanello, C., Zandonà, A., Paci, P., Capuani, G., et al. (2017) · Hepatology

Gut microbiota and healthLiver Disease Diagnosis and Treatment

Cited by 696

Repeatability of published microarray gene expression analyses

Ioannidis, J. P. A., Allison, D. B., Ball, C. A., Coulibaly, I., Cui, X., Culhane, A. C., Falchi, M., Furlanello, C., Game, L., Jurman, G., et al. (2009) · Nature Genetics

Gene expression and cancer classificationScientific Computing and Data Management

Cited by 551

Transparency and reproducibility in artificial intelligence

Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 540

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Wang, C., Gong, B., Bushel, P. R., Thierry-Mieg, J., Thierry-Mieg, D., Xu, J., Fang, H., Hong, H., Shen, J., Su, Z., et al. (2014) · Nature Biotechnology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 512

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

Jurman, G., Riccadonna, S., & Furlanello, C. (2012) · PLoS ONE

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 406

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Zhang, W., Yu, Y., Hertwig, F., Thierry-Mieg, J., Zhang, W., Thierry-Mieg, D., Wang, J., Furlanello, C., Devanarayan, V., Cheng, J., et al. (2015) · Genome Biology

Gene expression and cancer classificationNeuroblastoma Research and Treatments

Cited by 393

Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders

Mohammadian Rad, N., Van Laarhoven, T., Furlanello, C., & Marchiori, E. (2018) · Sensors

Anomaly Detection Techniques and ApplicationsContext-Aware Activity Recognition Systems

Cited by 76

Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals

Bizzego, A., Gabrieli, G., Furlanello, C., & Esposito, G. (2020) · Sensors

Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic Control

Cited by 25