Motivation Accurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these subgroups to cancer. The availability of multi-omics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. Results We develop PAMOGK (Pathway based Multi Omic Graph Kernel clustering) that integrates multi-omics patient data with existing biological knowledge on pathways. We develop a novel graph kernel that evaluates patient similarities based on a single molecular alteration type in the context of a pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, we use multi-view kernel clustering. Applying PAMOGK to kidney renal clear cell carcinoma (KIRC) patients results in four clusters with significantly different survival times (p-value = 1.24e-11). When we compare PAMOGK to eight other state-of-the-art multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition KIRC patients into groups with different survival distributions. The discovered patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. The pathways identified as important are highly relevant to KIRC. Availability github.com/tastanlab/pamogk Supplementary information Supplementary data are available at Bioinformatics online.

Over the last several years, the deployment of multi-core routers has grown rapidly. However, big data transfers are not leveraging the powerful multi-core routers to the extent possible, particularly in the key function of routing. Our main goal is to find a way to use these cores more effectively and efficiently in routing the big data transfers. We propose a novel approach to parallelize data transfers by using each core in the routers to calculate a separate shortest path. For each core, we generate a different "substrate" topology in order to allow shortest path calculations to find a different end-to-end (e2e) path. By abstracting a different topology for each core, we indirectly steer each core to calculate a different e2e path in parallel to each other. The e2e big data transfers can use these shortest paths obtained from each substrate topology to increase the total throughput. We present an initial evaluation of the concept.