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Amirali Aghazadeh

Showing results (1-10 of 10) with videos related to

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Bioinformatics (Oxford, England)|July 14, 2020
CRISPRL and: Interpretable large-scale inference of DNA repair landscape based on a spectral approachAmirali Aghazadeh, Orhan Ocal, Kannan Ramchandran
Proceedings of the National Academy of Sciences of the United States of America|December 23, 2021
On the sparsity of fitness functions and implications for learningDavid H Brookes, Amirali Aghazadeh, Jennifer Listgarten
PNAS Nexus|November 20, 2025
Discriminating abiotic and biotic organics in meteorite and terrestrial samples using machine learning on mass spectrometry dataDaniel Saeedi, Denise Buckner, Thomas A Walton, et al.
Proceedings of the National Academy of Sciences of the United States of America|March 4, 2021
Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysisVida Jamali, Cory Hargus, Assaf Ben-Moshe, et al.
Nature Communications|September 2, 2021
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functionsAmirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, et al.
Biorxiv : the Preprint Server for Biology|July 16, 2025
GOLF: A Generative AI Framework for Pathogenicity Prediction of Myocilin OLF VariantsThomas Walton, Darin Tsui, Lauren Fogel, et al.
Astrobiology|May 30, 2025
Challenges and Opportunities in Using Amino Acids to Decode Carbonaceous Chondrite and Asteroid Parent Body ProcessesJosé C Aponte, Hannah L McLain, Daniel Saeedi, et al.
Science Advances|October 6, 2016
Universal microbial diagnostics using random DNA probesAmirali Aghazadeh, Adam Y Lin, Mona A Sheikh, et al.
Nature Biotechnology|July 31, 2019
Large dataset enables prediction of repair after CRISPR-Cas9 editing in primary T cellsRyan T Leenay, Amirali Aghazadeh, Joseph Hiatt, et al.
Nature Communications|April 2, 2022
Current progress and open challenges for applying deep learning across the biosciencesNicolae Sapoval, Amirali Aghazadeh, Michael G Nute, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Bioinformatics (Oxford, England)|July 14, 2020
CRISPRL and: Interpretable large-scale inference of DNA repair landscape based on a spectral approachAmirali Aghazadeh, Orhan Ocal, Kannan Ramchandran
Proceedings of the National Academy of Sciences of the United States of America|December 23, 2021
On the sparsity of fitness functions and implications for learningDavid H Brookes, Amirali Aghazadeh, Jennifer Listgarten
PNAS Nexus|November 20, 2025
Discriminating abiotic and biotic organics in meteorite and terrestrial samples using machine learning on mass spectrometry dataDaniel Saeedi, Denise Buckner, Thomas A Walton, et al.
Proceedings of the National Academy of Sciences of the United States of America|March 4, 2021
Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysisVida Jamali, Cory Hargus, Assaf Ben-Moshe, et al.
Nature Communications|September 2, 2021
Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functionsAmirali Aghazadeh, Hunter Nisonoff, Orhan Ocal, et al.
Biorxiv : the Preprint Server for Biology|July 16, 2025
GOLF: A Generative AI Framework for Pathogenicity Prediction of Myocilin OLF VariantsThomas Walton, Darin Tsui, Lauren Fogel, et al.
Astrobiology|May 30, 2025
Challenges and Opportunities in Using Amino Acids to Decode Carbonaceous Chondrite and Asteroid Parent Body ProcessesJosé C Aponte, Hannah L McLain, Daniel Saeedi, et al.
Science Advances|October 6, 2016
Universal microbial diagnostics using random DNA probesAmirali Aghazadeh, Adam Y Lin, Mona A Sheikh, et al.
Nature Biotechnology|July 31, 2019
Large dataset enables prediction of repair after CRISPR-Cas9 editing in primary T cellsRyan T Leenay, Amirali Aghazadeh, Joseph Hiatt, et al.
Nature Communications|April 2, 2022
Current progress and open challenges for applying deep learning across the biosciencesNicolae Sapoval, Amirali Aghazadeh, Michael G Nute, et al.
Pageof 1