Biomonitoring and precision health in deep space supported by artificial intelligence

Autor: Ryan T. Scott, Lauren M. Sanders, Erik L. Antonsen, Jaden J. A. Hastings, Seung-min Park, Graham Mackintosh, Robert J. Reynolds, Adrienne L. Hoarfrost, Aenor Sawyer, Casey S. Greene, Benjamin S. Glicksberg, Corey A. Theriot, Daniel C. Berrios, Jack Miller, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Stuart Chalk, Guillermo M. Delgado-Aparicio, Melissa Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Hector Garcia Martin, Christopher E. Mason, Mona Matar, George I. Mias, Jerry G. Myers, Charlotte Nelson, Jonathan Oribello, Patricia Parsons-Wingerter, R. K. Prabhu, Amina Ann Qutub, Jon Rask, Amanda Saravia-Butler, Suchi Saria, Nitin Kumar Singh, Michael Snyder, Frank Soboczenski, Karthik Soman, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Jason H. Yang, Marinka Zitnik, Sylvain V. Costes

Publikováno v: Nature Machine Intelligence. 5:196-207

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c2f2c2831decdf4ca499077bf26aee51
https://doi.org/10.1038/s42256-023-00617-5

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design

Autor: Vincent Blay, Tijana Radivojevic, Jonathan E. Allen, Corey M. Hudson, Hector Garcia Martin

Publikováno v: Journal of chemical information and modeling, vol 62, iss 15

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses towards useful molecules. Here, we present MACAW (Molecular AutoenCoding Auto-Workaround), a tool that uses a novel approach to generate molecules pre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dfd380ade7a8daa98cc2142a91ba09d
https://doi.org/10.1021/acs.jcim.2c00229

Multi-parameter simultaneous extraction with a novel microwave sensor based on coupled resonators

Autor: Carlos G. Juan, Benjamin Potelon, Anyela Aquino, Héctor García-Martínez, Cédric Quendo

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)

Abstract This work presents a microwave resonant multi-parameter sensor devoted to the simultaneous extraction of three characteristics of a homogeneous solid sample: its dielectric permittivity, its loss tangent and its thickness. The device is comp

Externí odkaz: https://doaj.org/article/fdbfbb4e696f49ce8a79d6e281a27f27

ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design

Autor: Xavier B Tao, Sarah LaFrance, Yifei Xing, Alberto A Nava, Hector Garcia Martin, Jay D Keasling, Tyler W H Backman

Publikováno v: Tao, X B, LaFrance, S, Xing, Y, Nava, A A, Martin, H G, Keasling, J D & Backman, T W H 2023, ' ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design ', Nucleic Acids Research, vol. 51, no. D1, gkac1075 . https://doi.org/10.1093/nar/gkac1075

Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78520736423a4a9b83cf5370c089ab22
https://pubmed.ncbi.nlm.nih.gov/36416273

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Autor: Michael Krogh Jensen, Jens Nielsen, Jie Zhang, Eduardo Abeliuk, Zak Costello, Andrés Ramirez, Andrés Pérez-Manríquez, Benjamin Sanchez, Søren D. Petersen, Jay D. Keasling, Hector Garcia Martin, Michael J. Fero, Tijana Radivojevic, Yu Chen

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Nature Communications
Nature communications, vol 11, iss 1
Zhang, J, Petersen, S D, Radivojevic, T, Ramirez, A, Pérez-Manríquez, A, Abeliuk, E, Sánchez, B J, Costello, Z, Chen, Y, Fero, M J, Martin, H G, Nielsen, J, Keasling, J D & Jensen, M K 2020, ' Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism ', Nature Communications, vol. 11, no. 1, 4880 . https://doi.org/10.1038/s41467-020-17910-1

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54e52e82bd0714a12e1be42785be8cff
http://link.springer.com/article/10.1038/s41467-020-17910-1