Predicting genetic regulatory response using classification. - Manuel Middendorf; Anshul Kundaje; Chris Wiggins 001; Yoav Freund; Christina S. Leslie (ISMB/ECCB (Supplement of Bioinformatics), 2004)

Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response. - Manuel Middendorf; Anshul Kundaje; Chris Wiggins 001; Yoav Freund; Christina S. Leslie (Regulatory Genomics, 2004)

Discriminative topological features reveal biological network mechanisms. - Manuel Middendorf; Etay Ziv; Carter Adams; Jen Hom; Robin Koytcheff; Chaya Levovitz; Gregory Woods; Linda Chen; Chris Wiggins 001 (BMC Bioinform., 2004)

Motif Discovery Through Predictive Modeling of Gene Regulation. - Manuel Middendorf; Anshul Kundaje; Mihir Shah; Yoav Freund; Chris Wiggins 001; Christina S. Leslie (RECOMB, 2005)

Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules. - Anshul Kundaje; Manuel Middendorf; Feng Gao; Chris Wiggins 001; Christina S. Leslie (IEEE ACM Trans. Comput. Biol. Bioinform., 2005)

A classification-based framework for predicting and analyzing gene regulatory response. - Anshul Kundaje; Manuel Middendorf; Mihir Shah; Chris Wiggins 001; Yoav Freund; Christina S. Leslie (BMC Bioinform., 2006)

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. - Adam A. Margolin; Ilya Nemenman; Katia Basso; Chris Wiggins 001; Gustavo Stolovitzky; Riccardo Dalla Favera; Andrea Califano (BMC Bioinform., 2006)

Inferring the structure and scale of modular networks. - Jake M. Hofman; Chris H. Wiggins 001 (MLG@ICML, 2008)

Graphical models for inferring single molecule dynamics. - Jonathan E. Bronson; Jake M. Hofman; Jingyi Fei; Ruben L. Gonzalez; Chris H. Wiggins 001 (BMC Bioinform., 2010)

An Information-Theoretic Derivation of Min-Cut-Based Clustering. - Anil Raj; Chris H. Wiggins 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Learning "graph-mer" Motifs that Predict Gene Expression Trajectories in Development. - Xuejing Li; Casandra Panea; Chris H. Wiggins 001; Valerie Reinke; Christina S. Leslie (PLoS Comput. Biol., 2010)

Inference in Hidden Markov Models with Explicit State Duration Distributions. - Michael Dewar; Chris Wiggins 001; Frank D. Wood (IEEE Signal Process. Lett., 2012)

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. - Jan-Willem van de Meent; Jonathan E. Bronson; Frank D. Wood; Ruben L. Gonzalez; Chris Wiggins 001 (ICML (2), 2013)

Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer. - Francesco Abate; Sakellarios Zairis; Elisa Ficarra; Andrea Acquaviva; Chris H. Wiggins 001; Veronique Frattini; Anna Lasorella; Antonio Iavarone; Giorgio Inghirami; Raul Rabadan (BMC Syst. Biol., 2014)

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data. - Max Greenfeld; Jan-Willem van de Meent; Dmitri S. Pavlichin; Hideo Mabuchi; Chris H. Wiggins 001; Ruben L. Gonzalez; Daniel Herschlag (BMC Bioinform., 2015)

Learning probabilistic phenotypes from heterogeneous EHR data. - Rimma Pivovarov; Adler J. Perotte; Edouard Grave; John Angiolillo; Chris H. Wiggins 001; Noémie Elhadad (J. Biomed. Informatics, 2015)

Noise Expands the Response Range of the Bacillus subtilis Competence Circuit. - Andrew Mugler; Mark Kittisopikul; Luke Hayden; Jintao Liu; Chris H. Wiggins 001; Gürol M. Süel; Aleksandra M. Walczak (PLoS Comput. Biol., 2016)

Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2017)

Variational inference for the multi-armed contextual bandit. - Iñigo Urteaga; Chris Wiggins 001 (AISTATS, 2018)

Nonparametric Gaussian mixture models for the multi-armed contextual bandit. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2018)

(Sequential) Importance Sampling Bandits. - Iñigo Urteaga; Chris H. Wiggins 001 (CoRR, 2018)

An Agenda for Disinformation Research. - Nadya Bliss; Elizabeth Bradley; Joshua Garland; Filippo Menczer; Scott W. Ruston; Kate Starbird; Chris Wiggins 001 (CoRR, 2020)

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. - Kathy Li; Iñigo Urteaga; Chris H. Wiggins 001; Anna Druet; Amanda Shea; Virginia J. Vitzthum; Noémie Elhadad (npj Digit. Medicine, 2020)

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction. - Iñigo Urteaga; Kathy Li; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noemie Elhadad (MLHC, 2021)

A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data. - Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (CoRR, 2021)

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking. - Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (J. Am. Medical Informatics Assoc., 2021)

More Than Just Algorithms: A discussion with Alfred Spector, Peter Norvig, Chris Wiggins, Jeannette Wing, Ben Fried, and Michael Tingley. - Alfred Z. Spector; Peter Norvig; Chris Wiggins 001; Jeannette M. Wing; Ben Fried; Michael Tingley (ACM Queue, 2023)

Privacy Budget Tailoring in Private Data Analysis. - Daniel Alabi; Chris Wiggins 001 (Trans. Mach. Learn. Res., 2023)

More Than Just Algorithms. - Peter Norvig; Alfred Z. Spector; Chris Wiggins 001; Jeannette M. Wing; Ben Fried; Michael Tingley (Commun. ACM, 2023)