BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences
Jacqueline A. Valeri1,2,3,4, Luis R. Soenksen2,3,5, Katherine M. Collins3,6,7,8, Pradeep Ramesh3, George Cai3, Rani Powers3,9, Nicolaas M. Angenent-Mari1,2,3, Diogo M. Camacho3, Felix Wong1,2,4, Timothy K. Lu1,2,4,6,10, James J. Collins1,2,3,4,11,12
1Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
2Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
3Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
4Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
6Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
7Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
8Department of Engineering, University of Cambridge, Trumpington St, Cambridge, CB2 1PZ, UK
9Pluto Biosciences, Golden, CO 80402, USA
10Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
11Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA
12Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Cell Systems
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525-542.e9
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