| 1 | SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds | 2.8 | 13 | Citations (PDF) |
| 2 | ALLSTAR: inference of reliAble causaL ruLes between Somatic muTAtions and canceR phenotypes. | 3.2 | 0 | Citations (PDF) |
| 3 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 1.6 | 1 | Citations (PDF) |
| 4 | Bounding the family-wise error rate in local causal discovery using Rademacher averages | 2.6 | 1 | Citations (PDF) |
| 5 | caSPiTa: mining statistically significant paths in time series data from an unknown network | 1.9 | 1 | Citations (PDF) |
| 6 | The Impact of Global Structural Information in Graph Neural Networks Applications | 1.6 | 6 | Citations (PDF) |
| 7 | MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining | 2.8 | 8 | Citations (PDF) |
| 8 | SPRISS: approximating frequent k-mers by sampling reads, and applications. | 3.2 | 3 | Citations (PDF) |
| 9 | Technical perspective: Evaluating sampled metrics is challenging | 1.5 | 0 | Citations (PDF) |
| 10 | gRosSo: mining statistically robust patterns from a sequence of datasets | 1.9 | 1 | Citations (PDF) |
| 11 | Comparison of microbiome samples: methods and computational challenges | 4.1 | 26 | Citations (PDF) |
| 12 | Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation | 3.0 | 70 | Citations (PDF) |
| 13 | Fast Approximation of Frequent k-Mers and Applications to Metagenomics | 0.8 | 10 | Citations (PDF) |
| 14 | Identifying Drug Sensitivity Subnetworks with NETPHIX | 2.5 | 6 | Citations (PDF) |
| 15 | Efficient mining of the most significant patterns with permutation testing | 2.6 | 17 | Citations (PDF) |
| 16 | Mining Sequential Patterns with VC-Dimension and Rademacher Complexity | 1.5 | 12 | Citations (PDF) |
| 17 | MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension | 2.8 | 5 | Citations (PDF) |
| 18 | Enriched power of disease-concordant twin-case-only design in detecting interactions in genome-wide association studies | 2.1 | 5 | Citations (PDF) |
| 19 | People also looked at | 1.6 | 8 | Citations (PDF) |
| 20 | Efficient algorithms to discover alterations with complementary functional association in cancer | 2.0 | 13 | Citations (PDF) |
| 21 | Differentially mutated subnetworks discovery | 0.9 | 7 | Citations (PDF) |
| 22 | Efficient detection of differentially methylated regions using DiMmeR. | 3.2 | 9 | Citations (PDF) |
| 23 | Disease-Concordant Twins Empower Genetic Association Studies | 1.0 | 51 | Citations (PDF) |
| 24 | De novo pathway-based biomarker identification | 11.2 | 51 | Citations (PDF) |
| 25 | HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology | 3.2 | 83 | Citations (PDF) |
| 26 | Clustering uncertain graphs | 1.6 | 38 | Citations (PDF) |
| 27 | People also looked at | 1.6 | 31 | Citations (PDF) |
| 28 | Differentially Methylated Genomic Regions in Birth-Weight Discordant Twin Pairs | 1.0 | 23 | Citations (PDF) |
| 29 | Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing | 0.8 | 2 | Citations (PDF) |
| 30 | On the Sample Complexity of Cancer Pathways Identification | 0.8 | 6 | Citations (PDF) |
| 31 | CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer | 4.8 | 205 | Citations (PDF) |
| 32 | Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data | 0.8 | 33 | Citations (PDF) |
| 33 | Accurate Computation of Survival Statistics in Genome-Wide Studies | 2.0 | 27 | Citations (PDF) |
| 34 | Integrated Genomic Characterization of Papillary Thyroid Carcinoma | 23.8 | 2,948 | Citations (PDF) |
| 35 | Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine | 5.9 | 218 | Citations (PDF) |
| 36 | Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes | 14.2 | 970 | Citations (PDF) |
| 37 | Mutational landscape and significance across 12 major cancer types | 31.8 | 4,388 | Citations (PDF) |
| 38 | Ballast: A Ball-based Algorithm for Structural Motifs | 0.8 | 5 | Citations (PDF) |
| 39 | Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia | 24.4 | 4,921 | Citations (PDF) |
| 40 | An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets | 1.1 | 27 | Citations (PDF) |
| 41 | The mutational landscape of lethal castration-resistant prostate cancer | 31.8 | 2,449 | Citations (PDF) |
| 42 | Finding driver pathways in cancer: models and algorithms | 0.9 | 19 | Citations (PDF) |
| 43 | De novo discovery of mutated driver pathways in cancer | 3.2 | 428 | Citations (PDF) |
| 44 | Algorithms and Genome Sequencing: Identifying Driver Pathways in Cancer | 0.4 | 11 | Citations (PDF) |
| 45 | Algorithms for Detecting Significantly Mutated Pathways in Cancer | 0.8 | 454 | Citations (PDF) |
| 46 | MADMX: A Strategy for Maximal Dense Motif Extraction | 0.8 | 16 | Citations (PDF) |
| 47 | Mining top-K frequent itemsets through progressive sampling | 2.6 | 37 | Citations (PDF) |
| 48 | General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks | 0.9 | 0 | Citations (PDF) |