Efficient Incremental Mining of Top-K Frequent Closed Itemsets. - Andrea Pietracaprina; Fabio Vandin (Discovery Science, 2007)

MADMX: A Novel Strategy for Maximal Dense Motif Extraction. - Roberto Grossi; Andrea Pietracaprina; Nadia Pisanti; Geppino Pucci; Eli Upfal; Fabio Vandin (WABI, 2009)

Mining Top-K Frequent Itemsets Through Progressive Sampling - Andrea Pietracaprina; Matteo Riondato; Eli Upfal; Fabio Vandin (CoRR, 2010)

Mining top-K frequent itemsets through progressive sampling. - Andrea Pietracaprina; Matteo Riondato; Eli Upfal; Fabio Vandin (Data Min. Knowl. Discov., 2010)

De Novo Discovery of Mutated Driver Pathways in Cancer. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (RECOMB, 2011)

MADMX: A Strategy for Maximal Dense Motif Extraction. - Roberto Grossi; Andrea Pietracaprina; Nadia Pisanti; Geppino Pucci; Eli Upfal; Fabio Vandin (J. Comput. Biol., 2011)

Algorithms for Detecting Significantly Mutated Pathways in Cancer. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (J. Comput. Biol., 2011)

Workshop: Algorithms for discovery of mutated pathways in cancer. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (ICCABS, 2012)

Algorithms on evolving graphs. - Aris Anagnostopoulos; Ravi Kumar 001; Mohammad Mahdian; Eli Upfal; Fabio Vandin (ITCS, 2012)

Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer. - Fabio Vandin; Patrick Clay; Eli Upfal; Benjamin J. Raphael (Pacific Symposium on Biocomputing, 2012)

Finding Driver Pathways in Cancer: Models and Algorithms. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (Algorithms Mol. Biol., 2012)

Algorithms and Genome Sequencing: Identifying Driver Pathways in Cancer. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (Computer, 2012)

An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets. - Adam Kirsch; Michael Mitzenmacher; Andrea Pietracaprina; Geppino Pucci; Eli Upfal; Fabio Vandin (J. ACM, 2012)

Identifying significant mutations in large cohorts of cancer genomes. - Fabio Vandin; Eli Upfal; Benjamin J. Raphael (ICCABS, 2013)

Genome-Wide Survival Analysis of Somatic Mutations in Cancer. - Fabio Vandin; Alexandra Papoutsaki; Benjamin J. Raphael; Eli Upfal (RECOMB, 2013)

Controlling False Positives in Frequent Itemsets Mining through the VC-Dimension - Matteo Riondato; Fabio Vandin (CoRR, 2013)

Ballast: A Ball-based Algorithm for Structural Motifs. - Lu He; Fabio Vandin; Gopal Pandurangan; Chris Bailey-Kellogg (J. Comput. Biol., 2013)

Finding the True Frequent Itemsets. - Matteo Riondato; Fabio Vandin (SDM, 2014)

CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer. - Mark D. M. Leiserson; Hsin-Ta Wu; Fabio Vandin; Benjamin J. Raphael (RECOMB, 2015)

Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data. - Benjamin J. Raphael; Fabio Vandin (J. Comput. Biol., 2015)

Space-efficient parallel algorithms for combinatorial search problems. - Andrea Pietracaprina; Geppino Pucci; Francesco Silvestri 001; Fabio Vandin (J. Parallel Distributed Comput., 2015)

Accurate Computation of Survival Statistics in Genome-Wide Studies. - Fabio Vandin; Alexandra Papoutsaki; Benjamin J. Raphael; Eli Upfal (PLoS Comput. Biol., 2015)

Reconstructing Hidden Permutations Using the Average-Precision (AP) Correlation Statistic. - Lorenzo De Stefani; Alessandro Epasto; Eli Upfal; Fabio Vandin (AAAI, 2016)

Finding Mutated Subnetworks Associated with Survival in Cancer. - Tommy Hansen; Fabio Vandin (RECOMB, 2016)

The Second Decade of the International Conference on Research in Computational Molecular Biology (RECOMB). - Farhad Hormozdiari; Fereydoun Hormozdiari; Carl Kingsford; Paul Medvedev; Fabio Vandin (RECOMB, 2016)

An Efficient Branch and Cut Algorithm to Find Frequently Mutated Subnetworks in Cancer. - Anna Bomersbach; Marco Chiarandini; Fabio Vandin (WABI, 2016)

On the Sample Complexity of Cancer Pathways Identification. - Fabio Vandin; Benjamin J. Raphael; Eli Upfal (J. Comput. Biol., 2016)

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and MethylationEPIC data processing. - Diogo Almeida; Ida Skov; Jesper Lund; Afsaneh Mohammadnejad; Artur Silva; Fabio Vandin; Qihua Tan; Jan Baumbach; Richard Röttger (J. Integr. Bioinform., 2016)

Efficient detection of differentially methylated regions using DiMmeR. - Diogo Almeida; Ida Skov; Artur Silva; Fabio Vandin; Qihua Tan; Richard Röttger; Jan Baumbach (Bioinform., 2017)

Clustering Uncertain Graphs. - Matteo Ceccarello; Carlo Fantozzi; Andrea Pietracaprina; Geppino Pucci; Fabio Vandin (Proc. VLDB Endow., 2017)

Permutation Strategies for Mining Significant Sequential Patterns. - Andrea Tonon; Fabio Vandin (ICDM, 2019)

SPuManTE: Significant Pattern Mining with Unconditional Testing. - Leonardo Pellegrina; Matteo Riondato; Fabio Vandin (KDD, 2019)

Hypothesis Testing and Statistically-sound Pattern Mining. - Leonardo Pellegrina; Matteo Riondato; Fabio Vandin (KDD, 2019)

Differentially mutated subnetworks discovery. - Morteza Chalabi Hajkarim; Eli Upfal; Fabio Vandin (Algorithms Mol. Biol., 2019)

Efficient algorithms to discover alterations with complementary functional association in cancer. - Rebecca Sarto Basso; Dorit S. Hochbaum; Fabio Vandin (PLoS Comput. Biol., 2019)

CoExpresso: assess the quantitative behavior of protein complexes in human cells. - Morteza Chalabi Hajkarim; Vasileios Tsiamis; Lukas Käll; Fabio Vandin; Veit Schwämmle (BMC Bioinform., 2019)

Mining Sequential Patterns with VC-Dimension and Rademacher Complexity. - Diego Santoro; Andrea Tonon; Fabio Vandin (Algorithms, 2020)

Are Graph Convolutional Networks Fully Exploiting Graph Structure? - Davide Buffelli; Fabio Vandin (CoRR, 2020)

Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation. - Davide Buffelli; Fabio Vandin (CoRR, 2020)

A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings. - Davide Buffelli; Fabio Vandin (CoRR, 2020)

Efficient mining of the most significant patterns with permutation testing. - Leonardo Pellegrina; Fabio Vandin (Data Min. Knowl. Discov., 2020)

Fast Approximation of Frequent k-Mers and Applications to Metagenomics. - Leonardo Pellegrina; Cinzia Pizzi; Fabio Vandin (J. Comput. Biol., 2020)

MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension. - Matteo Riondato; Fabio Vandin (ACM Trans. Knowl. Discov. Data, 2020)

odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks. - Ilie Sarpe; Fabio Vandin (CIKM, 2021)

Scalable Distributed Approximation of Internal Measures for Clustering Evaluation. - Federico Altieri; Andrea Pietracaprina; Geppino Pucci; Fabio Vandin (SDM, 2021)

PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts. - Ilie Sarpe; Fabio Vandin (SDM, 2021)

Comparison of microbiome samples: methods and computational challenges. - Matteo Comin; Barbara Di Camillo; Cinzia Pizzi; Fabio Vandin (Briefings Bioinform., 2021)

Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach. - Davide Buffelli; Fabio Vandin (IJCNN, 2022)

SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. - Davide Buffelli; Pietro Lió; Fabio Vandin (NeurIPS, 2022)

Discovering significant evolutionary trajectories in cancer phylogenies. - Leonardo Pellegrina; Fabio Vandin (Bioinform., 2022)

SPRISS: approximating frequent k-mers by sampling reads, and applications. - Diego Santoro; Leonardo Pellegrina; Matteo Comin; Fabio Vandin (Bioinform., 2022)

Technical perspective: Evaluating sampled metrics is challenging. - Fabio Vandin (Commun. ACM, 2022)

The Impact of Global Structural Information in Graph Neural Networks Applications. - Davide Buffelli; Fabio Vandin (Data, 2022)

gRosSo: mining statistically robust patterns from a sequence of datasets. - Andrea Tonon; Fabio Vandin (Knowl. Inf. Syst., 2022)

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining. - Leonardo Pellegrina; Cyrus Cousins; Fabio Vandin; Matteo Riondato (ACM Trans. Knowl. Discov. Data, 2022)

VC-dimension and Rademacher Averages of Subgraphs, with Applications to Graph Mining. - Paolo Pellizzoni; Fabio Vandin (ICDE, 2023)

Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages (Extended Abstract). - Dario Simionato; Fabio Vandin (IJCAI, 2023)

caSPiTa: mining statistically significant paths in time series data from an unknown network. - Andrea Tonon; Fabio Vandin (Knowl. Inf. Syst., 2023)

Fast and Accurate Triangle Counting in Graph Streams Using Predictions. - Cristian Boldrin; Fabio Vandin (ICDM, 2024)

Scalable Rule Lists Learning with Sampling. - Leonardo Pellegrina; Fabio Vandin (KDD, 2024)

Scalable Temporal Motif Densest Subnetwork Discovery. - Ilie Sarpe; Fabio Vandin; Aristides Gionis (KDD, 2024)

SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds. - Leonardo Pellegrina; Fabio Vandin (ACM Trans. Knowl. Discov. Data, 2024)

Efficient Discovery of Significant Patterns with Few-Shot Resampling. - Leonardo Pellegrina; Fabio Vandin (Proc. VLDB Endow., 2024)

Bounding the family-wise error rate in local causal discovery using Rademacher averages. - Dario Simionato; Fabio Vandin (Data Min. Knowl. Discov., 2024)

Efficient Approximate Temporal Triangle Counting in Streaming with Predictions. - Giorgio Venturin; Ilie Sarpe; Fabio Vandin (CoRR, 2025)