Ye Chen
5 Papers
12 Citations
Ye Chen is an academic researcher from Google. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 1, co-authored 4 publications.
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Papers
Deep learning and automated Cell Painting reveal Parkinson’s disease-specific signatures in primary patient fibroblasts
Lauren Schiff,Migliori B,Ye Chen,Carter D,Bonilla C,Hall J,Minjie Fan,Edmund Tam,Sara Ahadi,Brodie Fischbacher,Anton Geraschenko,Christopher J. Hunter,Subhashini Venugopalan,DesMarteau S,Arunachalam Narayanaswamy,Samson T. Jacob,Zan Armstrong,Ferrarotto P,Brian Williams,Buckley-Herd G,Jon Hazard,Goldberg J,Marc Coram,Otto R,Edward A. Baltz,Andres-Martin L,Pritchard O,Duren-Lubanski A,Katie Reggio,Bauer L,Raeka S. Aiyar,Elizabeth Schwarzbach,Daniel Paull,Scott Noggle,Monsma Fj,Marc Berndl,Yang Sj,Bjarki Johannesson +37 more
TL;DR: A novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning is presented, able to confidently separate LRRK2 and sporadic PD lines from healthy controls and supporting the capacity of this platform for PD modeling and drug screening applications.
Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts
Lauren Schiff,Bianca Migliori,Ye Chen,Deidre Carter,Caitlyn Bonilla,Jenna Hall,Minjie Fan,Edmund Tam,Sara Ahadi,Brodie Fischbacher,Anton Geraschenko,Christopher J. Hunter,Subhashini Venugopalan,Sean DesMarteau,Arunachalam Narayanaswamy,Selwyn Jacob,Zan Armstrong,Peter Ferrarotto,Brian Williams,Geoff Buckley-Herd,Jon Hazard,Jordan Goldberg,Marc Coram,Reid Otto,Edward A. Baltz,Laura Andres-Martin,Orion Pritchard,Alyssa Duren-Lubanski,Ameya Daigavane,Kathryn Reggio,Lauren Bauer,Raeka S. Aiyar,Elizabeth Schwarzbach,Daniel Paull,Scott Noggle,Frederick J. Monsma,Marc Berndl,Samuel Yang,Bjarki Johannesson +38 more
TL;DR: In this article, an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, cell painting, and deep learning is presented. But the platform is not suitable for the detection of individual-specific variation with high fidelity across batches and plate layouts.
Protseq: An Investigation of High-Throughput, Single-Molecule Protein Sequencing via Amino Acid Conversion into DNA Barcodes
Jessica Hong,Michael Gibbons,Ali Bashira,Diana Wu,Shirley Jing Shao,Zachary Cutts,Mariya Chavarha,Ye Chen,Lauren Schiff,Mikelle Foster,Victoria A. Church,Llyke Ching,Sara Ahadi,Anna Hieu-Thao Le,Alexander H. Tran,Michelle Dimon,Marc Coram,Brian Williams,Phillip Jess,Marc Berndl,Annalisa Pawlosky +20 more
TL;DR: This work demonstrates successful barcode capture and DNA-barcode chain sequencing for DNA-DNA and nanobody-protein binding pairs and introduces a binder discovery pipeline called Target-Switch SELEX to discover aptamer binders.
1
Patent
Methods and compositions for protein and peptide sequencing
Annalisa Pawlosky,Zachary Cutts,Jessica Hong,Shirley Jing Shao,Anna Le,Diana Terri Wu,Sara Ahadi,Alexander Julian Tran,Ali Bashir,Michael Gibbons,Mariya Chavarha,Emma Katherine Costa,Phillip Jess,Victoria A. Church,Marc Berndl,Ye Chen,Samuel Yang,Michelle Dimon +17 more
- 18 Mar 2021
Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts
Lauren Schiff,Bianca Migliori,Ye Chen,Deidre Lynn Carter,Caitlyn Bonilla,Jenna Hall,Minjie Fan,Edmund Tam,Sara Ahadi,Brodie Fischbacher,Anton Geraschenko,Christopher J. Hunter,Subhashini Venugopalan,Sean DesMarteau,Arunachalam Narayanaswamy,Selwyn Jacob,Zan Armstrong,Peter Ferrarotto,Brian Williams,Geoff Buckley-Herd,Jon Hazard,Jordan Goldberg,Marc Coram,Reid Otto,Edward A. Baltz,Laura Andres-Martin,Orion Pritchard,Alyssa Duren-Lubanski,Ameya Daigavane,Kathryn S. Reggio,Lauren Bauer,Raeka S. Aiyar,Elizabeth Schwarzbach,Daniel Paull,Scott Noggle,Frederick J. Monsma,Marc Berndl,Samuel Yang,Bjarki Johannesson +38 more
TL;DR: In this article , an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning was presented to detect individual-specific variation with high fidelity across batches and plate layouts.