Scalable Similarity Matching in Streaming Time Series. - Alice Marascu; Suleiman A. Khan; Themis Palpanas (PAKDD (2), 2012)

Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs. - Suleiman A. Khan; Ali Faisal; John Mpindi; Juuso A. Parkkinen; Tuomo Kalliokoski; Antti Poso; Olli-P. Kallioniemi; Krister Wennerberg; Samuel Kaski (BMC Bioinform., 2012)

Bayesian Group Factor Analysis. - Seppo Virtanen; Arto Klami; Suleiman A. Khan; Samuel Kaski (AISTATS, 2012)

Kernelized Bayesian Matrix Factorization. - Mehmet Gönen; Suleiman A. Khan; Samuel Kaski (ICML (3), 2013)

Bayesian Multi-view Tensor Factorization. - Suleiman A. Khan; Samuel Kaski (ECML/PKDD (1), 2014)

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis. - Suleiman A. Khan; Seppo Virtanen; Olli-P. Kallioniemi; Krister Wennerberg; Antti Poso; Samuel Kaski (Bioinform., 2014)

Bayesian multi-view models for data-driven drug response analysis. - Suleiman A. Khan (2015)

Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization. - Muhammad Ammad-ud-din; Suleiman A. Khan; Disha Malani; Astrid Murumägi; Olli-P. Kallioniemi; Tero Aittokallio; Samuel Kaski (Bioinform., 2016)

Bayesian multi-tensor factorization. - Suleiman A. Khan; Eemeli Leppäaho; Samuel Kaski (Mach. Learn., 2016)

Convex Factorization Machine for Toxicogenomics Prediction. - Makoto Yamada; Wenzhao Lian; Amit Goyal 001; Jianhui Chen; Kishan Wimalawarne; Suleiman A. Khan; Samuel Kaski; Hiroshi Mamitsuka; Yi Chang 001 (KDD, 2017)

Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. - Muhammad Ammad-ud-din; Suleiman A. Khan; Krister Wennerberg; Tero Aittokallio (Bioinform., 2017)

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach. - Mehreen Ali; Suleiman A. Khan; Krister Wennerberg; Tero Aittokallio (Bioinform., 2018)

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System. - Muhammad Ammad-ud-din; Elena Ivannikova; Suleiman A. Khan; Were Oyomno; Qiang Fu; Kuan Eeik Tan; Adrian Flanagan (CoRR, 2019)

Federated Multi-view Matrix Factorization for Personalized Recommendations. - Adrian Flanagan; Were Oyomno; Alexander Grigorievskiy; Kuan Eeik Tan; Suleiman A. Khan; Muhammad Ammad-ud-din (ECML/PKDD (2), 2020)

Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation. - Muhammad Irfan Khan; Mojtaba Jafaritadi; Esa Alhoniemi; Elina Kontio; Suleiman A. Khan (BrainLes@MICCAI (2), 2021)

Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation. - Muhammad Irfan Khan; Mohammad Ayyaz Azeem; Esa Alhoniemi; Elina Kontio; Suleiman A. Khan; Mojtaba Jafaritadi (BrainLes@MICCAI (2), 2022)

RegAgg: A Scalable Approach for Efficient Weight Aggregation in Federated Lesion Segmentation of Brain MRIs. - Muhammad Irfan Khan; Esa Alhoniemi; Elina Kontio; Suleiman A. Khan; Mojtaba Jafaritadi (FMEC, 2023)

Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation. - Muhammad Irfan Khan; Esa Alhoniemi; Elina Kontio; Suleiman A. Khan; Mojtaba Jafaritadi (CoRR, 2023)

Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation. - Muhammad Irfan Khan; Elina Kontio; Suleiman A. Khan; Mojtaba Jafaritadi (CoRR, 2024)

Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation. - Muhammad Irfan Khan; Elina Kontio; Suleiman A. Khan; Mojtaba Jafaritadi (CoRR, 2024)