J
Jeffrey T. Chang
Researcher at University of Texas Health Science Center at Houston
Publications - 165
Citations - 13689
Jeffrey T. Chang is an academic researcher from University of Texas Health Science Center at Houston. The author has contributed to research in topics: Cancer & Breast cancer. The author has an hindex of 44, co-authored 142 publications receiving 10902 citations. Previous affiliations of Jeffrey T. Chang include University of Texas at Austin & Stanford University.
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Journal ArticleDOI
Biopython: freely available Python tools for computational molecular biology and bioinformatics
Peter J. A. Cock,Tiago Antao,Jeffrey T. Chang,Brad Chapman,Cymon J. Cox,Andrew Dalke,Iddo Friedberg,Thomas Hamelryck,Frank Kauff,Bartosz Wilczyński,Michiel J. L. de Hoon +10 more
TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
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Oncogenic pathway signatures in human cancers as a guide to targeted therapies.
Andrea Bild,Guang Yao,Jeffrey T. Chang,Quanli Wang,Anil Potti,Dawn Chasse,Mary Beth Joshi,David H. Harpole,Johnathan M. Lancaster,Andrew Berchuck,John A. Olson,Jeffrey R. Marks,Holly K. Dressman,Mike West,Joseph R. Nevins +14 more
TL;DR: It is shown that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways and linked with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogens pathway signatures to guide the use of targeted therapeutics.
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High-dimensional sparse factor modeling: Applications in gene expression genomics
TL;DR: These case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers.
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High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types.
Daniel J. McGrail,Patrick G. Pilie,Naim U. Rashid,Leonie Voorwerk,Maarten Slagter,Marleen Kok,Eric Jonasch,Mustafa Khasraw,Amy B. Heimberger,Bora Lim,NT Ueno,Jennifer K. Litton,Renata Ferrarotto,Jeffrey T. Chang,S. L. Moulder,Sy Lin +15 more
TL;DR: In this article, the authors compared approaches to determine TMB and identify the correlation between predicted neoantigen load and CD8 T cells, and found that TMB-H tumors exhibited a 39.8% ORR to ICB [95% confidence interval (CI) 34.9-44.8], which was significantly higher than that observed in low TMB (TMB-L) tumors.
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Integrating genotype and phenotype information: an overview of the PharmGKB project
Teri E. Klein,Jeffrey T. Chang,Mildred K. Cho,K. L. Easton,R. Fergerson,Micheal Hewett,Zhen Lin,Yueyi Liu,S. Liu,Diane E. Oliver,Daniel L. Rubin,F. Shafa,Joshua M. Stuart,Russ B. Altman +13 more
TL;DR: This work has shown that individuals with a genetic variation for the enzyme thiopurine methyltransferase do not need lower doses of 6-mercaptopurine for effective treatment as normal doses can be lethal.