Baldi, Pierre

Assessing the accuracy of prediction algorithms for classification: an overview

Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics

Machine Learning in BioinformaticsMachine Learning and Data Classification

Cited by 2,111

Neural networks and principal component analysis: Learning from examples without local minima

Baldi, P., & Hornik, K. (1989) · Neural Networks

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 1,383

Bayesian surprise attracts human attention

Itti, L., & Baldi, P. (2009) · Vision Research

Visual Attention and Saliency DetectionVisual perception and processing mechanisms

Cited by 1,224

Muscle insulin sensitivity and glucose metabolism are controlled by the intrinsic muscle clock

Dyar, K. A., Ciciliot, S., Wright, L. E., Biensø, R. S., Tagliazucchi, G. M., Patel, V. R., Forcato, M., Paz, M. I. P., Gudiksen, A., Solagna, F., et al. (2014) · Molecular Metabolism

Circadian rhythm and melatoninMetabolism, Diabetes, and Cancer

Cited by 454

Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems

Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., & Gentine, P. (2021) · Physical Review Letters

Model Reduction and Neural NetworksNeural Networks and Applications

Cited by 451

The dropout learning algorithm

Baldi, P., & Sadowski, P. (2014) · Artificial Intelligence

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 340

Time of exercise specifies the impact on muscle metabolic pathways and systemic energy homeostasis

Sato, S., Basse, A. L., Schönke, M., Chen, S., Samad, M., Altıntaş, A., Laker, R. C., Dalbram, E., Barrès, R., Baldi, P., et al. (2019) · Cell Metabolism

Circadian rhythm and melatoninMetabolism, Diabetes, and Cancer

Cited by 298

Partitioning Circadian Transcription by SIRT6 Leads to Segregated Control of Cellular Metabolism

Masri, S., Rigor, P., Cervantes, M., Ceglia, N., Sebastian, C., Xiao, C., Roqueta-Rivera, M., Deng, C., Osborne, T. F., Mostoslavsky, R., et al. (2014) · Cell

Circadian rhythm and melatoninSirtuins and Resveratrol in Medicine

Cited by 292

Lung adenocarcinoma distally rewires hepatic circadian homeostasis

Masri, S., Papagiannakopoulos, T., Kinouchi, K., Liu, Y., Cervantes, M., Baldi, P., Jacks, T., & Sassone-Corsi, P. (2016) · Cell

Circadian rhythm and melatoninMetabolism, Diabetes, and Cancer

Cited by 234

Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis

Sato, S., Dyar, K. A., Treebak, J. T., Jepsen, S. L., Ehrlich, A. M., Ashcroft, S. P., Trost, K., Kunzke, T., Prade, V. M., Small, L., et al. (2022) · Cell Metabolism

Circadian rhythm and melatoninExercise and Physiological Responses

Cited by 203

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality

Lee, C. K., Hofer, I., Gabel, E., Baldi, P., & Cannesson, M. (2018) · Anesthesiology

Cardiac, Anesthesia and Surgical OutcomesMachine Learning in Healthcare

Cited by 185

Deep learning in biomedical data science

Baldi, P. (2018) · Annual Review of Biomedical Data Science

Machine Learning in HealthcareMachine Learning in Bioinformatics

Cited by 119

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data

Hill, B. L., Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., et al. (2019) · British Journal of Anaesthesia

Cardiac, Anesthesia and Surgical OutcomesMachine Learning in Healthcare

Cited by 103

The DNA modification N6-methyl-2’-deoxyadenosine (m6dA) drives activity-induced gene expression and is required for fear extinction

Li, X., Zhao, Q., Wei, W., Lin, Q., Magnan, C., Emami, M. R., Wearick-Silva, L. E., Viola, T. W., Marshall, P. R., Yin, J., et al. (2019) · Nature Neuroscience

Epigenetics and DNA MethylationMemory and Neural Mechanisms

Cited by 71

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction

Sadowski, P., Fooshee, D., Subrahmanya, N., & Baldi, P. (2016) · Journal of Chemical Information and Modeling

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 65

Visual adaptation and novelty responses in the superior colliculus: Adaptation in the SC

Boehnke, S. E., Berg, D. J., Marino, R. A., Baldi, P. F., Itti, L., & Munoz, D. P. (2011) · European Journal of Neuroscience

Visual perception and processing mechanismsNeural dynamics and brain function

Cited by 60

Inner and Outer Recursive Neural Networks for Chemoinformatics Applications

Urban, G., Subrahmanya, N., & Baldi, P. (2018) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 25

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Jo, T., Kim, J., Bice, P., Huynh, K., Wang, T., Arnold, M., Meikle, P. J., Giles, C., Kaddurah-Daouk, R., Saykin, A. J., et al. (2023) · eBioMedicine

Dementia and Cognitive Impairment ResearchMetabolomics and Mass Spectrometry Studies

Cited by 15