Selected Papers:

Oufattole N., Bergamaschi T., Kolo A., McDermott M., Stultz CM. MEDS-torch: An ML Pipleine for Inductive Experiments for EHR Medical Foundation Models. NeurIPS 2024 Workshop on Time Series in the Age of Large Models.

Alam R., Bergamaschi T., Stultz CM. Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AI. International Conference on Body Sensor Networks (IEEE BSN) 2024

Davarmanesh P., Alam R., Stultz CM., Detection of Non-lateral Acute Myocardial Infarction Using Deep Learning on Lead I ECG Data.  AHA 2024

Stultz CM. What is AI and Why Should I Care? Heart Rhythm Society.  In press. DOI: 10.1016/j.hrthm.2024.08.001

Oufattole N., Jeong H., Mcdermott M., Balagopalan A., Jangeesingh B., Ghassemi M., Stultz CM. Event-Based Contrastive Learning for Medical Time Series.     MLHC 2024.

Alam R., Aguirre A., Stultz CM. Detecting QT prolongation From a Single-lead ECG With Deep Learning. PLOS Digital Health 3(6): e0000539. https://doi-org.ezproxy.canberra.edu.au/10.1371/journal.pdig.0000539

Schlesinger D., Alam R., Ringel R., Pomerantsev E., Devreddy S, Shah R. Garasic J., Stultz CM.  Artificial Intelligence for Outpatient Hemodynamic Monitoring with a Wearable ECG Monitor. medRxiv 2024.04.01.24304487; doi: https://doi-org.ezproxy.canberra.edu.au/10.1101/2024.04.01.24304487

Jeong H., Oufattole N., Balagopalan A., Mcdermott M., Jangeesingh B., Ghassemi M., Stultz CM. Event-Based Contrastive Learning for Medical Time Series. Accepted at Unifying Representations in Neural Models Workshop in NeurIPS 2023. https://arxiv.org/abs/2312.10308

Stultz CM., Machine Learning for Risk Prediction: Does One Size Really Fit All? JACC:Advances https://authors-elsevier-com.ezproxy.canberra.edu.au/sd/article/S2772963X23004805

Raghu A., Chandak P., Ridwan A., Guttag J., Stultz CM. Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. International Conference on Machine Learning (ICML), PMLR 202:28531-28548, 2023

Ridwan Alam, Aguirre, Stultz Cm. Deep Learning for Estimating QT Intervals Using a Single Lead. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’23), to appear

Raghu A., Schlesinger D., Pomerantsev E., Devireddy S., Shah P., Garasic J., Guttag J., Stultz CM.  ECG-guided Non-invasive Estimation of Pulmonary Congestion in Patients with Heart Failure.  Nature Scientific Reports.  (2023) 13:3923 https://rdcu.be/c7dPY

Identifying Aortic Stenosis with a Single Parasternal Long Axis Video using Deep Learning. Dai., W., Hung J., Namasivayam M., Nazzari H., Stultz CM. Journal of the American Society of Echocardiography, 2022 https://doi-org.ezproxy.canberra.edu.au/10.1016/j.echo.2022.10.014.

Reply: More Than Meets the AI: Electrocardiograms in Heart Failure Prognosics. DE Schlesinger DE, CM Stultz CM. JACC Advances 2022

Contrastive Pretraining for Multimodal Medical Time Series. Raghu A., Chandak P., Alam R., Guttag J., Stultz CM. NeurIPS 2022 Workshop TS4H

Unsupervised Deep Metric Learning for the Inference of Hemodynamic Values with Electrocardiogram Signals.  Jeong H., Ghassemi M., Stultz CM. NeurIPS 2022 Workshop TS4H

Predicting Outcomes in Patients with Aortic Stenosis Using Machine Learning: the Aortic Stenosis Risk (ASteRisk) Score.  Namasivayam M., Myers P.D., Guttag J.V., Capoulade R., PhD, Pibarot P., Picard M.H., MD, Hung J., Stultz C. M., OpenHeart 2022;9:e001990. doi:10.1136/openhrt-2022-001990

Raghu A., Shanmugam D., Pomerantsev E., Guttag G., Stultz CM., Data Augmentation for Electrocardiograms.  CHIL ’22: Proceedings of the Conference on Health, Inference, and Learning, 2022

Schlesinger D., Diamant N., Raghu A., Reinertsen E., Young K., Batra P., Pomerantz E., Stultz CM. A deep learning model for inferring elevated pulmonary capillary wedge pressures from the 12-lead electrocardiogram, JACC: Advances 2022 Mar, 1 (1) 1–11.

Diamant N., Reinertsen E., Song S., Aguirre A., Stultz CM., Batra P. Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling. PLOS Computational Biology 18(2): e1009862. https://doi-org.ezproxy.canberra.edu.au/10.1371/journal.pcbi.1009862

Morales M., van den Boomen M., Nguyen C., Kalpathy-Cramer J., Rosen BR., Stultz CM., Izquierdo-Garcia D., Catana C., DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics .Frontiers in Cardiovascular Medicine September 2021, vol. 8th Article 730316, https://doi-org.ezproxy.canberra.edu.au/10.3389/fcvm.2021.730316

Raghu A., Guttag J., Young K., Pomerantsev E., Dalca A., Stultz CM. Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction. CHIL ’21: Proceedings of the Conference on Health, Inference, and Learning April 2021 Pages 95–104 https://doi-org.ezproxy.canberra.edu.au/10.1145/3450439.3451869

Stultz CM., Deep learning in cardiology: All models are wrong, but some are useful, Trends in Cardiovascular Medicine,  2020, ISSN 1050-1738, https://doi-org.ezproxy.canberra.edu.au/10.1016/j.tcm.2020.12.001.

Schlesinger D., Stultz CM. Deep Learning for Cardiovascular Risk Stratification.  Current Treatment Options in Cardiovascular Medicine, 22, 15 (2020). https://doi-org.ezproxy.canberra.edu.au/10.1007/s11936-020-00814-0

Dai W., Stultz CM. Quantifying Common Support Between Multiple Treatment Groups Using a Contrastive Variational Autoendcoder.  NeurIPs ML4H Workshop, 2020. Published in Proceedings of Machine Learning Research 136:41–52, 2020

Myers P.D., Ng K., Severson K., Kartoun U., Dai W., Huang W., Anderson F., Stultz CM. Identifying Unreliable Predictions in Clinical Risk Models, npj Digit. Med. 3, 8 (2020). https://doi-org.ezproxy.canberra.edu.au/10.1038/s41746-019-0209-7

Myers P.D., Huang W., Anderson F., Stultz CM. Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome. Nature Scientific Reports 9, 14631 (2019) doi:10.1038/s41598-019-50933-3

Dai W., Kenney Ng K., Severson KA., Huang W., Anderson F., Stultz CM. Generative Oversampling with a Contrastive Variational Autoencoder.  International Conference on Data Mining, 2019

Stultz CM. The Advent of Clinically Useful Deep Learning. Journal of the American College of Cardiology, Electrophysiology (2019), vol. 5 no. 5 587-589

Bowman SEJ,  Backman LRF, Bjorka RE, Andorfer M., Yorid S., Carusod A., 3, Stultz CM., Catherine L. Drennan CL. Solution structure and biochemical characterization of a spare part protein that restores activity to an oxygen-damaged glycyl radical enzyme., Journal of Bioinorganic Chemistry https://doi-org.ezproxy.canberra.edu.au/10.1007/s00775-019-01681-2

Myers PD., Scirica BM., Stultz Cm., Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.  Scientific Reports 7, 12692

Burger VM., Vandervelde A., Hendrix J., Konijnenberg A., Sobott F., Lorisand R., Stultz CM.,  Hidden States with Disordered Regions of the CcdA Antitoxin Protein.  Journal of the American Chemical Society, 139 (7): 2693-2701

Liu Y., Scirica B.M., Stultz CM.*, Guttag JV. Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome. Nature Scientific Reports 6, Article Number 34540, 2016

Burger V., Arenas D., Stultz CM. A Structure-free Method for Quantifying Conformational Flexibility in proteins. Scientific Reports 6, Article Number 29040, 2016.

Ziegler Z., Schmidt M., Gurry T., Burger V., Stultz CM., Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins. Bioinformatics first published online April 19, 2016doi:10.1093/bioinformatics/btw200

Burger V., Nolasco D., Stultz CM., Expanding the Range of Protein Function at the Far end of the Order-Structure Continuum.  Journal of Biological ChemistryJ. Biol. Chem. 2016 291:6706-.doi:10.1074/jbc.R115.692590

Liu Y., Chuang K., Liang F., Su H., Stultz CM, Guttag JV., Leveraging Feature Hierarchy and Bootstrap Lasso to Reduce Dimensionality and Improve Interpretability”. NIPS 2015 Workshop on Machine Learning in Healthcare

Gurry T, Fisher CK, Schmidt M, Stultz CM. Analyzing Ensembles of Amyloid Proteins Using Bayesian Statistics. Methods Mol Biol. 2016;1345:269-80. doi:10.1007/978-1-4939-2978-8_17. PubMed PMID: 26453218.

Gurry T., Stultz CM. The Mechanism of Amyloid-Beta Fibril Elongation.  Biochemistry 53, 6981−6991, DOI: 10.1021/bi500695g, 2014

Burger VM., Gurry T., Stultz CM. Intrinsically Disordered Proteins: Where Computation Meets Experiment.  Polymers 6(10) 2684-2719, 2014

Zhong C., Gurry T., Cheng Al., Downey J., Deng Z., Stultz CM., Lu TK., Biologically Inspired Engineering of Strong, Self-Assembling, and Multi-Functional Underwater Adhesives.   Nature Nanotechnology  9, 858–866,  2014

Linder D., Gurry T., Stultz CM. Towards a Consensus in Protein Structure Nomenclature. in Intrinsically Disordered Proteins. 2014 (Comment)

Yun L., Syed Z., Scirica BM, Morrow DA, Guttag JV, Stultz CM. ECG Morphological Variability in Beat-space for Risk Stratification after Acute Coronary Syndrome. Journal of the American Heart Association, 2014;3:e000981 doi:10.1161/JAHA.114.000981

Gee PS., Yuksel D., Stultz CM., Ingber DE. SLLISWD Sequence in the 10FNIII Domain Initiates Fibronectin Fibrillogenesis. Journal of Biological Chemistry 288: 21329-21340, 2013.

Lu KG., Stultz CM., Insight into the Degradation of Type-I Collagen Fibrils by MMP-8. Journal of Molecular Biology 425(10): 1815-1825, 2013.

Fisher C., Ullman O., Stultz CM., Comparative Studies of Disordered Proteins with Similar Sequences: Application to AB40 and AB42. Biophysical Journal 104: 1546-1555, 2013.

Gurry T., Ullman O., Fisher C., Perovic I., Pochapsky T., Stultz CM., The dynamic structure of alpha-synuclein multimers. Journal of the American Chemical Society 135: 3865-3872, 2013.

Liu J., Schubert C., Xiaoqin F., Houdusse A., Fourniel F., Stultz CM., Moores C., Walsh, C. Molecular Basis for Specific Regulation of Neuronal Kinesin-3 Motors by Doublecortin Family Proteins.  Molecular Cell 47: 707-721, 2012

Walker S., Ullman O., Stultz CM. Using Intramolecular Disulfide Bonds in Tau Protein to Deduce Structural Features of Aggregation-resistant Conformations. Journal of Biological Chemistry, 287(12):9591-9600, 2012

Fisher CK., Ullman O., Stultz CM.  Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations.  Pacific Symposium on Biocomputing 17:82-93, 2012

Ullman O., Fisher C., Stultz CM. Explaining the Structural Plasticity of α-Synuclein. Journal of the American Chemical Society 133 (48), 19536–19546, 2011

Syed Z., Stultz CM., Scirica BM., Guttag JV. Computationally Generated Cardiac Biomarkers for Risk Stratification Following Acute Coronary Syndrome.  Science: Translational Medicine 28 September Vol 3 Issue 102 102ra95, 2011.

Fisher CK., Stultz CM. Protein Structure along the order-disorder continuum. Journal of the American Chemical Society. 133 (26): 10022–10025, 2011.

Fisher CK., Stultz CM. Constructing Ensembles for Intrinsically Disordered Proteins. Current Opinion in Structural Biology. 21:426-431, 2011.

Chen MM., Bartlett AI., Nerenberg PS., Friel CT., Hackenberger CPR., Stultz CM., Radford SE., Imperiali B. Perturbing the folding energy landscape of the bacterial immunity protein Im7 by site-specific N-linked glycosylation. Proceedings of the National Academy of Sciences 107 (52) 22528-22533, 2010.

Fisher CK., Huang A., Stultz CM. Modeling Intrinsically Disordered Proteins with Bayesian Statistics. Journal of the American Chemical Society 132, 14919-14927, 2010.

Phillips C., Schreiter E., Stultz CM., Drennan C. Structural Basis of Low Affinity Nickel Binding to the Nickel-Responsive Transcription Factor NikR from Escherichia coli.  Biochemistry 49, 7830-7838, 2010.

Phillips C., Stultz CM., Drennan C. Searching for the nik operon: how a ligand-responsive transcription factor hunts for its DNA binding site. Biochemistry 49, 7757-7763, 2010.

Gurry T., Nerenberg P., Stultz CM. The Contribution of Inter-chain Salt Bridges to Triple Helical Stability in Collagen. Biophysical Journal 98(11) pp. 2634-2643, 2010.

Salsas-Escat R., Nerenberg P., Stultz CM. Cleavage site specificity and conformational selection in type I collagen degradation. Biochemistry, 49, 4147–4158, 2010.

Salsas-Esat R., Stultz CM., Conformational Selection and Collagenolysis in Type III Collagen. Proteins: Structure, Function, and Bioinformatics 78, 325-335 (DOI 10.1002/prot.22545), 2010

Syed Z., Stultz CM., Kellis M., Indyk P. Guttag J. Motif Discovery in Physiological Datasets: A Methodology for Inferring Predictive Elements. ACM Transactions on Knowledge Discovery from Data 4(1), Article 2, 1-23, 2010.

Phillips C., Nerenberg P., Drennan C., Stultz CM. The Physical Basis of Metal Binding Specificity in E. coli NikR. Journal of the American Chemical Society131(29), 10220-10228 (DOI: 10.1021/ja9026314), 2009.

Schubert C., Stultz CM. The Multi-Copy Simultaneous Search Methodology: A Fundamental Tool for Structure-Based Drug Design. Journal of Computer Aided Molecular Design (DOI 10.1007/s10822-009-9287-y), 2009.

Huang A., Stultz CM. Finding Order within Disorder: Elucidating the Structure of Proteins Associated with Neurodegenerative Disease. Future Medicinal Chemistry1(3):467-482, 2009.

Syed Z., Sung P., Sciric BM., Morrow, DA.  Stultz CM., Guttag JV. Spectral Energy of ECG Morphologic Differences to Predict Death.  Cardiovascular Engineering (DOI 10.1007/s10558-009-9066-3), 2009.

Yoon M., Venkatachalam V., Huang A., Choi B., Stultz CM., Chou JJ. Residual structure within the disordered C-terminal segment of p21Waf1/Cip1/Sdi1 and its implications for molecular recognition. Protein Science 18(2):337-347, 2009.

Syed S.., Scirica BM., Mohanavelu S., Sung P., Cannon CP., Stone PH., Stultz CM., Guttag JG. Relation of Death Within 90 Days of Non-ST-Elevation Acute Coronary Syndromes to Variability in Electrocardiographic Morphology. American Journal of Cardiology, 103(3):307-311 (DOI: 10.1016/j.amjcard.2008.09.099), 2009.

Huang A., Stultz CM. The Effect of a ΔK280 Mutation on the Unfolded State of a Microtubule Binding Repeat in Tau. PLoS Computational Biology4(8): e1000155 doi:10.1371/journal.pcbi.1000155 .

Nerenberg P., Stultz CM. Differential Unfolding of α1 and α2 chains in Type I Collagen and Collagenolysis. JMB 382, 246-256, 2008.

Gee PS. Gee, Ingber DE., Stultz CM. Fibronectin Unfolding Revisited: Modeling Cell Traction-mediated Unfolding of the Tenth Type-III Repeat Running Title: Fibronectin Unfolding Revisited.  PLoS ONE, 3(6): e2373. doi:10.1371/journal.pone.0002373.

Nerenberg P., Salsas-Escat R., Stultz CM. Do collagenases unwind triple-helical collagen prior to peptide bond hydrolysis? Reinterpreting experimental observations with mathematical models. Proteins: Structure, Function, and Bioinformatics, 70: 1154-1161, 2008.

Nerenberg P., Salsas-Escat R., Stultz CM. Collagen – A Necessary Accomplice in the Metastatic Process. Cancer Genomics & Proteomics 4: 319-328 (2007).

Salsas-Escat R., Stultz CM. The Molecular Mechanics of Collagen Degradation:  Implications for Human Disease. Experimental Mechanics (Published online ahead of print DOI10.1007/s11340-007-9105-1).

Syed Z., Guttag J., and Stultz CM. Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge. 2007; EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 67938, 16 pages.

Thomas A., Edelman ER., Stultz CM. Collagen Fragments Modulate Innate Immunity. 2007; Exp. Biol. & Med. 232: 406-411

Huang, A. & Stultz CM. Conformational Sampling with Implicit Solvent Models: Application to the PHF6 Peptide in Tau Protein. 2007; Biophys. J.  92: 34–45.

Stultz, CM. The Folding Mechanism of Collagen-like Model Peptides Explored through Detailed Simulations. Protein Science. 2006; 15: 2166-2177.

Stultz CM. Cosmology and proteins: landscape of possibilities.  Nature Physics. 2006; 2(6): 357.

Stultz CM., Karplus M. In Fragment-based Approaches in Drug Discovery. Methods and Principles in Medicinal Chemistry v34. eds. Jahnke and Erlanson, Wiley-VCH, Weinheim. 2006. pg. 125-148.

Stultz CM. An Assessment of Potential of Mean Force Calculations with Implicit Solvent Models. J. Chem. Phys B. 2004; 108:16525-16532.

Stultz CM, Edelman ER. A Structural Model that Explains the Effects of Hyperglycemia on Collagenolysis. Biophys. J. 2003; 85:2198-2204.

Stultz CM. Localized Unfolding of Collagen Explains Collagenase Cleavage Near Imino-Poor Sites. J. Mol. Biol. 2002;319:997-1003.

Stultz CM, Levin AD, Edelman ER. Phosphorylation Induced Conformational Changes in a MAP-Kinase Substrate: Implications for Tyrosine Hydroxylase Activation. J. Biol. Chem. 2002;277:47653-47661.

Stultz CM, Karplus M. Dynamic Ligand Design and Combinatorial Optimization. Proteins, Structure Function and Genetics. 2000; 40: 259-289.

Stultz CM, Karplus M. MCSS Functionality Maps for a Flexible Protein. Proteins, Structure Function and Genetics. 1999;37:512-529.

Stultz CM, Karplus M. On the Potential Surface of the Locally Enhanced Sampling Approximation. Journal of Chemical Physics. 1998; 109: 8809-8815.

Stultz CM, Nambudripad R, Lathrop R, White JV. Predicting Protein Structure with Probabilistic Models. In: Protein Structural Biology in Bio-Medical Research , Vol. 22B of Advances in Molecular and Cell Biology series editor: E. E. Bittar, Guest Editors: N.M. Allewell and C. Woodward.  Greenwich CT: JAI Press ;1997. p. 447-506.

White JV, Stultz CM, Smith TF. Protein Classification by Stochastic Modeling and Optimal Filtering of Amino-Acid Sequences. Mathematical Biosciences. 1994; 119: 35-75.

Stultz CM, White JV, Smith TF. Structural Analysis Based on State Space Modeling. Protein Science. 1993; 2: 305-314.