| scholarly article | Q13442814 |
| P818 | arXiv ID | 1302.6977 |
| P8978 | DBLP publication ID | journals/bmcbi/RyslikCCMZ13 |
| P356 | DOI | 10.1186/1471-2105-14-190 |
| P8608 | Fatcat ID | release_32imjp22g5dfjbtpcfl4c35olu |
| P932 | PMC publication ID | 3691676 |
| P698 | PubMed publication ID | 23758891 |
| P5875 | ResearchGate publication ID | 237814225 |
| P50 | author | Yorgo Modis | Q38320743 |
| P2093 | author name string | Hongyu Zhao | |
| Kei-Hoi Cheung | |||
| Yuwei Cheng | |||
| Gregory A Ryslik | |||
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| P275 | copyright license | Creative Commons Attribution 2.0 Generic | Q19125117 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P4510 | describes a project that uses | SQL | Q47607 |
| P304 | page(s) | 190 | |
| P577 | publication date | 2013-06-13 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | Utilizing protein structure to identify non-random somatic mutations | |
| P478 | volume | 14 |
| Q28550391 | A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces |
| Q30360645 | A graph theoretic approach to utilizing protein structure to identify non-random somatic mutations. |
| Q30364303 | A spatial simulation approach to account for protein structure when identifying non-random somatic mutations |
| Q37096363 | Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes |
| Q39286993 | Characterization of potential driver mutations involved in human breast cancer by computational approaches |
| Q38675855 | Comparison of algorithms for the detection of cancer drivers at subgene resolution |
| Q28080523 | Computational characterisation of cancer molecular profiles derived using next generation sequencing |
| Q38265995 | Deciphering oncogenic drivers: from single genes to integrated pathways |
| Q38429590 | Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information. |
| Q36435002 | DriverDBv2: a database for human cancer driver gene research |
| Q64236136 | DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies |
| Q30388270 | Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure |
| Q34207474 | Expanding the computational toolbox for mining cancer genomes |
| Q64115144 | Functional characterization of 3D protein structures informed by human genetic diversity |
| Q34423170 | Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach |
| Q37689928 | Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine |
| Q36272766 | Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression |
| Q34667055 | Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes |
| Q62648881 | Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers |
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| Q30388511 | Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes |
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