25 citations · 70 across the 7 of their papers we have counts for
8 papers
Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
John Zobolas, Anne-Marie George, Alberto López +3
Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability f…
Tutorial on survival modeling with applications to omics data
Zhi Zhao, John Zobolas, Manuela Zucknick +1
Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and tre…
Evaluation of statistical approaches for association testing in noisy drug screening data
Petr Smirnov, Ian Smith, Zhaleh Safikhani +9
dentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to…
Characterizing the Quality of Insight by Interactions: A Case Study
Chen He, Luana Micallef, Liye He +3
Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying in…
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization
Muhammad Ammad-ud-din, Suleiman A. Khan, Disha Malani +4
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sam…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
RPA: Probabilistic analysis of probe performance and robust summarization
Leo Lahti, Laura L. Elo, Tero Aittokallio +1
Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood. Data-driven analysis of…
Improving the false nearest neighbors method with graphical analysis
T. Aittokallio, M. Gyllenberg, J. Hietarinta +2
We introduce a graphical presentation for the false nearest neighbors (FNN) method. In the original method only the percentage of false neighbors is computed without regard to the…