Motivation: In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. Results: We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas. Availability and Implementation: : The code for this work is available at https://github.com/olganikolova/gbgfa. Contact: : [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

We present a technique/or clustering measurements such that high-dimensional parameter estimation problems can be simplified. The key idea is to find rows of the measurement Jacobian whose rank is significantly less than its width. Such a set of rows gives a cluster of measurements which is affected only by a subset of the parameter space. This cluster can be used independently from other measurements to isolate parameter decisions. Unlike static partitioning techniques, the method presented dynamically generates clusters at each step of the estimation. This achieves substantial computational reductions, even for problems which cannot be partitioned in the traditional sense. The technique is applied to the task of tracking camera motions in real-time and video sequences are used to compare the resulting system to previous methods.

We present a method for efficiently generating a representation of a multi-modal posterior probability distribution. The technique combines ideas from RANSAC and particle filtering such that the 3D visual tracking problem can be partitioned into two levels, while maintaining multiple hypotheses throughout. A simple texture change-point detector finds multiple hypotheses for the position of image edgels. From these, multiple locations for each scene edge are generated. Finally, we determine the best pose of the whole structure. While the multi-modal representation is strongly related to particle filtering techniques, this approach is driven by data from the image. Hence the resulting system is able to perform robust visual tracking of all six degrees of freedom in real time. Real video sequences are used to compare the complete tracking system to previous systems.