Daniel Delubac
12 Papers
22 Citations
Daniel Delubac is an academic researcher. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 4, co-authored 8 publications.
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Papers
Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Nathan Wan,David E. Weinberg,Tzu-Yu Liu,Katherine E. Niehaus,Eric A. Ariazi,Daniel Delubac,Ajay Kannan,Brandon White,Mitch Bailey,Marvin Bertin,Nathan Boley,Derek Bowen,James Cregg,Adam Drake,Riley Ennis,Signe Fransen,Erik Gafni,Loren Hansen,Yaping Liu,Gabriel Otte,Jennifer Pecson,Brandon J. Rice,Gabriel E. Sanderson,Aarushi Sharma,John St. John,Catherina Tang,Abraham Tzou,Leilani Young,Girish Putcha,Imran S. Haque +29 more
TL;DR: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort and the possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies.
Patient-derived micro-organospheres enable clinical precision oncology.
Sheng Ding,C. Hsu,Zhao-xia Wang,Naveen Natesh,R. E. Millen,Marcos Negrete,N Giroux,Grecia O. Rivera,Anders B. Dohlman,Shree Bose,Tomer Rotstein,Kassandra V. Spiller,Athena Yeung,Zhiguo Sun,Chongming Jiang,Rui Xi,Benjamin D. Wilkin,Peggy M. Randon,Ian O. Williamson,Daniel Nelson,Daniel Delubac,Sehwa Oh,Gabrielle Rupprecht,James Isaacs,Jing Jia,Chao-Hu Chen,John Paul Shen,Scott Kopetz,Shannon J. McCall,Amber Smith,Nikolce Gjorevski,Antje Walz,Scott J. Antonia,Estelle Marrer-Berger,Hans Clevers,David S. Hsu,Xiling Shen +36 more
TL;DR: In this paper , the authors used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology.
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Rapid tissue prototyping with micro-organospheres
Zhao-xia Wang,Matteo Boretto,R. E. Millen,Naveen Natesh,Elena S. Reckzeh,C. Hsu,Marcos Negrete,Haipei Yao,William Quayle,Brook E. Heaton,Alfred T. Harding,Shree Bose,Else Driehuis,Joep Beumer,Grecia O. Rivera,Ravian L. van Ineveld,Donald Gex,Jessica DeVilla,Daisong Wang,Jens Puschhof,Maarten H. Geurts,Athena Yeung,Cait E. Hamele,Amber Smith,Eric D. Bankaitis,Kun Xiang,Sheng Ding,Daniel Nelson,Daniel Delubac,Anne C. Rios,Ralph Abi-Hachem,David W. Jang,Bradley J. Goldstein,Carolyn M. Glass,Nicholas S. Heaton,David S. Hsu,Hans Clevers,Xiling Shen +37 more
TL;DR: Micro-organospheres (MOSs) as mentioned in this paper are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells.
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Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Nathan Wan,David E. Weinberg,Tzu-Yu Liu,Katherine E. Niehaus,Daniel Delubac,Ajay Kannan,Brandon White,Eric A. Ariazi,Mitch Bailey,Marvin Bertin,Nathan Boley,Derek Bowen,James Cregg,Adam Drake,Riley Ennis,Signe Fransen,Erik Gafni,Loren Hansen,Yaping Liu,Gabriel Otte,Jennifer Pecson,Brandon J. Rice,Gabriel E. Sanderson,Aarushi Sharma,John St. John,Catherina Tang,Abraham Tzou,Leilani Young,Girish Putcha,Imran S. Haque +29 more
TL;DR: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort and the possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies.
Patent
High-throughput sample processing systems and methods of use
Kyle Lapham,James Cregg,Daniel Delubac,Stuart Ira Glaser +3 more
- 02 Jun 2015
TL;DR: In this article, high-throughput sample processing systems and waste management systems, and methods of using the same are discussed. But they do not discuss the use of the same for the same.
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