J
Junfei Zhao
Researcher at Columbia University
Publications - 83
Citations - 3011
Junfei Zhao is an academic researcher from Columbia University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 21, co-authored 57 publications receiving 1735 citations. Previous affiliations of Junfei Zhao include Vanderbilt University & Vanderbilt University Medical Center.
Papers
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Journal ArticleDOI
Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma.
Junfei Zhao,Andrew X. Chen,Robyn D. Gartrell,Andrew M. Silverman,Luis Aparicio,Tim Chu,Darius Bordbar,David Shan,Jorge Samanamud,Aayushi Mahajan,Ioan Filip,Rose Orenbuch,Morgan Goetz,Jonathan T. Yamaguchi,Michael Cloney,Craig Horbinski,Rimas V. Lukas,Jeffrey Raizer,Ali I Rae,Jinzhou Yuan,Peter Canoll,Jeffrey N. Bruce,Yvonne M. Saenger,Peter A. Sims,Fabio M. Iwamoto,Adam M. Sonabend,Raul Rabadan +26 more
TL;DR: This study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment.
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TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes
TL;DR: TSGene 2.0, which is the only available database for TSGs, provides the most updated TSGs and their features in pan-cancer.
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New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis.
Yuan Hou,Junfei Zhao,William R. Martin,Asha R. Kallianpur,Asha R. Kallianpur,Mina K. Chung,Mina K. Chung,Mina K. Chung,Lara Jehi,Nima Sharifi,Nima Sharifi,Serpil C. Erzurum,Serpil C. Erzurum,Charis Eng,Feixiong Cheng,Feixiong Cheng,Feixiong Cheng +16 more
TL;DR: This study suggested that ACE2 or TMPRSS2 DNA polymorphisms were likely associated with genetic susceptibility of COVID-19, which calls for a human genetics initiative for fighting the COVID -19 pandemic.
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Efficient methods for identifying mutated driver pathways in cancer
TL;DR: This study proposes two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer, and proposes an integrative model to combine mutation and expression data.
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Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes
TL;DR: A review of methods and computational tools developed during the past several years for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine may help investigators find an appropriate tool.