Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Alex X. Lu"'
Autor:
Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, Sarah Alamdari, James Y. Zou, Alex X. Lu, Ava P. Amini
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, d
Externí odkaz:
https://doaj.org/article/5afb156d8bca45489f6193c74ddc6d26
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 6, p e1010238 (2022)
A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as
Externí odkaz:
https://doaj.org/article/cb97a63ed41d4fd2a040177bf0b5297a
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 9, p e1007348 (2019)
Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically
Externí odkaz:
https://doaj.org/article/687354cd37c843249bb050c254ef608f
Autor:
Alex X Lu, Yolanda T Chong, Ian Shen Hsu, Bob Strome, Louis-Francois Handfield, Oren Kraus, Brenda J Andrews, Alan M Moses
Publikováno v:
eLife, Vol 7 (2018)
The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-th
Externí odkaz:
https://doaj.org/article/53a7eae98e7f4b198f6b9657f1119bc4
Autor:
Alexander Lin, Alex X. Lu
Data collected by high-throughput microscopy experiments are affected by batch effects, stemming from slight technical differences between experimental batches. Batch effects significantly impede machine learning efforts, as models learn spurious tec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d0f29748b398052cc14286228a19ec2
https://doi.org/10.1101/2022.10.14.512286
https://doi.org/10.1101/2022.10.14.512286
Pretrained protein sequence language models have been shown to improve the performance of many functional and structural prediction tasks, and are now routinely integrated into bioinformatics tools. However, these models largely rely on the Transform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8593a84a16d0d6cccb89979bce5122d
https://doi.org/10.1101/2022.05.19.492714
https://doi.org/10.1101/2022.05.19.492714
A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6566ffa05809bacf6bb9a0e93c4cfb3e
https://doi.org/10.1101/2021.07.29.454330
https://doi.org/10.1101/2021.07.29.454330
Autor:
Alex X. Lu, Wengong Jin, Regina Barzilay, Samuel Goldman, Caroline Uhler, Tommi S. Jaakkola, Karren Yang
Publikováno v:
CVPR
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0055055b3cc967188a830109d6b56eb
https://doi.org/10.1109/cvpr46437.2021.00662
https://doi.org/10.1109/cvpr46437.2021.00662
Publikováno v:
Microscopy and Microanalysis. 26:690-692
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15853740de942bd8c091b3f92fa3890e
https://doi.org/10.1017/s1431927620015548
https://doi.org/10.1017/s1431927620015548
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 9, p e1007348 (2019)
PLoS Computational Biology
PLoS Computational Biology
Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70ae72cff05e6548785d3cedb010df82
https://doaj.org/article/687354cd37c843249bb050c254ef608f
https://doaj.org/article/687354cd37c843249bb050c254ef608f