proteomics


Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. 1

Our next meeting will be at 3:00 on October 10th, in room 3160 of the Discovery building. Our Selected paper is Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.
The abstract is as follows.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC

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Multitask matrix completion for learning protein interactions across diseases

Four our next meeting on 3/28/2016 we have selected Multitask matrix completion for learning protein interactions across diseases by Kshirsagar et al. The abstract is as follows.

Disease causing pathogens such as viruses, introduce their proteins into the host cells where they interact with the host’s proteins enabling the virus to replicate inside the host. These interactions be- tween pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or bio- logically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different, but related viruses: Hepatitis C, Ebola virus and Influenza A. Our multi- task matrix completion based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various in- teractions. We obtain upto a 39% improvement in predictive performance over prior state-of-the-art models. We show how our model’s parame- ters can be interpreted to reveal both general and specific interaction- relevant characteristics of the viruses. Our code and data is available at: http://www.cs.cmu.edu/~mkshirsa/bsl_mtl.tgz

We look forward to seeing all who can come. Feel free to begin our discussion in the comments section below.