216 citations · 399 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Convex Factorization Machine for Regression
Makoto Yamada, Wenzhao Lian, Amit Goyal +6
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Specifically, we employ a linear+quadratic model and r…
Bayesian multi-tensor factorization
Suleiman A. Khan, Eemeli Leppäaho, Samuel Kaski
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem gene…
Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis
Suleiman A Khan, Seppo Virtanen, Olli P Kallioniemi +3
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how…
Kernelized Bayesian Matrix Factorization
Mehmet Gönen, Suleiman A. Khan, Samuel Kaski
We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability to work with multiple side information sources expressed as different kernels. Kernel…
Bayesian Group Factor Analysis
Seppo Virtanen, Arto Klami, Suleiman A. Khan +1
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor anal…