Analysis of Gene Sets Based on the Underlying Regulatory Network. - Ali Shojaie; George Michailidis (J. Comput. Biol., 2009)

Penalized Principal Component Regression on Graphs for Analysis of Subnetworks. - Ali Shojaie; George Michailidis (NIPS, 2010)

Discovering graphical Granger causality using the truncating lasso penalty. - Ali Shojaie; George Michailidis (Bioinform., 2010)

Using random walks to identify cancer-associated modules in expression data. - Deanna Petrochilos; Ali Shojaie; John H. Gennari; Neil F. Abernethy (BioData Min., 2013)

The Cluster Elastic Net for High-Dimensional Regression With Unknown Variable Grouping. - Daniela M. Witten; Ali Shojaie; Fan Zhang (Technometrics, 2014)

The cluster graphical lasso for improved estimation of Gaussian graphical models. - Kean Ming Tan; Daniela M. Witten; Ali Shojaie (Comput. Stat. Data Anal., 2015)

Network granger causality with inherent grouping structure. - Sumanta Basu; Ali Shojaie; George Michailidis (J. Mach. Learn. Res., 2015)

Causal Structure Learning with Reduced Partial Correlation Thresholding. - Arjun Sondhi; Ali Shojaie (DSAA, 2016)

Network-based pathway enrichment analysis with incomplete network information. - Jing Ma; Ali Shojaie; George Michailidis (Bioinform., 2016)

Graphical Models for Non-Negative Data Using Generalized Score Matching. - Shiqing Yu; Mathias Drton; Ali Shojaie (AISTATS, 2018)

Wavelet regression and additive models for irregularly spaced data. - Asad Haris; Ali Shojaie; Noah Simon (NeurIPS, 2018)

Gene set analysis methods: a systematic comparison. - Ravi Mathur; Daniel M. Rotroff; Jun Ma 032; Ali Shojaie; Alison A. Motsinger-Reif (BioData Min., 2018)

Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction. - Nafiseh Sedaghat; Mahmood Fathy; Mohammad Hossein Modarressi; Ali Shojaie (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

A comparative study of topology-based pathway enrichment analysis methods. - Jing Ma; Ali Shojaie; George Michailidis (BMC Bioinform., 2019)

Integer Programming for Learning Directed Acyclic Graphs from Continuous Data. - Hasan Manzour; Simge Küçükyavuz; Ali Shojaie (CoRR, 2019)

Bayesian hidden Markov models for dependent large-scale multiple testing. - Xia Wang; Ali Shojaie; Jian Zou (Comput. Stat. Data Anal., 2019)

The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks. - Arjun Sondhi; Ali Shojaie (J. Mach. Learn. Res., 2019)

Generalized Score Matching for Non-Negative Data. - Shiqing Yu; Mathias Drton; Ali Shojaie (J. Mach. Learn. Res., 2019)

Statistical Significance in High-dimensional Linear Mixed Models. - Lina Lin; Mathias Drton; Ali Shojaie (FODS, 2020)

Differential Network Analysis: A Statistical Perspective. - Ali Shojaie (CoRR, 2020)

Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks. - Simge Küçükyavuz; Ali Shojaie; Hasan Manzour; Linchuan Wei (CoRR, 2020)

Statistical Inference for Networks of High-Dimensional Point Processes. - Xu Wang; Mladen Kolar; Ali Shojaie (CoRR, 2020)

CorDiffViz: an R package for visualizing multi-omics differential correlation networks. - Shiqing Yu; Mathias Drton; Daniel E. L. Promislow; Ali Shojaie (BMC Bioinform., 2021)

Granger Causality: A Review and Recent Advances. - Ali Shojaie; Emily B. Fox (CoRR, 2021)

Definite Non-Ancestral Relations and Structure Learning. - Wenyu Chen; Mathias Drton; Ali Shojaie (CoRR, 2021)

Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes. - Xu Wang; Ali Shojaie (Entropy, 2021)

netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis. - Michael Hellstern; Jing Ma; Kun Yue; Ali Shojaie (PLoS Comput. Biol., 2021)

The Convex Mixture Distribution: Granger Causality for Categorical Time Series. - Alex Tank; Xiudi Li; Emily B. Fox; Ali Shojaie (SIAM J. Math. Data Sci., 2021)

Integer Programming for Learning Directed Acyclic Graphs from Continuous Data. - Hasan Manzour; Simge Küçükyavuz; Hao-Hsiang Wu; Ali Shojaie (INFORMS J. Optim., 2021)

Nonparametric Causal Structure Learning in High Dimensions. - Shubhadeep Chakraborty; Ali Shojaie (Entropy, 2022)

Generalized Sparse Additive Models. - Asad Haris; Noah Simon; Ali Shojaie (J. Mach. Learn. Res., 2022)

Neural Granger Causality. - Alex Tank; Ian Covert; Nicholas J. Foti; Ali Shojaie; Emily B. Fox (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Directed Graphical Models and Causal Discovery for Zero-Inflated Data. - Shiqing Yu; Mathias Drton; Ali Shojaie (CLeaR, 2023)

Interaction Models and Generalized Score Matching for Compositional Data. - Shiqing Yu; Mathias Drton; Ali Shojaie (LoG, 2023)

Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks. - Simge Küçükyavuz; Ali Shojaie; Hasan Manzour; Linchuan Wei; Hao-Hsiang Wu (J. Mach. Learn. Res., 2023)

On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure. - Yunhua Xiang; Tianyu Zhang; Xu Wang; Ali Shojaie; Noah Simon (J. Mach. Learn. Res., 2023)

Causal Structural Learning via Local Graphs. - Wenyu Chen; Mathias Drton; Ali Shojaie (SIAM J. Math. Data Sci., 2023)

Learning Directed Acyclic Graphs from Partial Orderings. - Ali Shojaie; Wenyu Chen (CoRR, 2024)

An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models. - Tong Xu; Armeen Taeb; Simge Küçükyavuz; Ali Shojaie (CoRR, 2024)

Spectral Differential Network Analysis for High-Dimensional Time Series. - Michael Hellstern; Byol Kim; Zaïd Harchaoui; Ali Shojaie (AISTATS, 2025)

Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci. - Zeinab Mousavi; Marios Arvanitis; Thuyvy Duong; Jennifer A. Brody; Alexis Battle; Nona Sotoodehnia; Ali Shojaie; Dan E. Arking; Joel S. Bader (PLoS Comput. Biol., 2025)