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Cryo-EM Structures of Human Drosha and DGCR8 in Complex with Primary MicroRNA

Molecular cell, 2020-05, Vol.78 (3), p.411-422.e4

2020

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RNA Specificity and Autoregulation of DDX17, a Modulator of MicroRNA Biogenesis

Cell reports (Cambridge), 2019-12, Vol.29 (12), p.4024-4035.e5

2019

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Heme enables proper positioning of Drosha and DGCR8 on primary microRNAs

Nature communications, 2017-11, Vol.8 (1), p.1737-10, Article 1737

2017

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Converting tabular data into images for deep learning with convolutional neural networks

Scientific reports, 2021-05, Vol.11 (1), p.11325-11, Article 11325

2021

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Enhancing the Prefusion Conformational Stability of SARS-CoV-2 Spike Protein Through Structure-Guided Design

Frontiers in immunology, 2021-04, Vol.12, p.660198

2021

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Geometric parameters that affect the behavior of logic-gated CAR T cells

Frontiers in immunology, 2024, Vol.15, p.1304765-1304765

2024

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Learning curves for drug response prediction in cancer cell lines

BMC bioinformatics, 2021-05, Vol.22 (1), p.252-252, Article 252

2021

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What Do We Know About Shear Wave Dispersion in Normal and Steatotic Livers?

Ultrasound in medicine & biology, 2015-05, Vol.41 (5), p.1481-1487

2015

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AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Scientific reports, 2023-02, Vol.13 (1), p.2105-2105, Article 2105

2023

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Publisher Correction: Heme enables proper positioning of Drosha and DGCR8 on primary microRNAs

Nature communications, 2018-09, Vol.9 (1), p.3852-1, Article 3852

2018

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Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Frontiers in medicine, 2023-02, Vol.10, p.1086097-1086097

2023

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Mouse Liver Dispersion for the Diagnosis of Early-Stage Fatty Liver Disease: A 70-Sample Study

Ultrasound in medicine & biology, 2014-04, Vol.40 (4), p.704-713

2014

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Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancers, 2023-12, Vol.16 (1), p.50

2023

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Publisher Correction: Converting tabular data into images for deep learning with convolutional neural networks

Scientific reports, 2021-07, Vol.11 (1), p.1-1, Article 14036

2021

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A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Cancers, 2024-01, Vol.16 (3), p.530

2024

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Protocol for high-throughput cloning, expression, purification, and evaluation of bispecific antibodies

STAR protocols, 2022-06, Vol.3 (2), p.101428-101428, Article 101428

2022

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Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

Genes, 2020-09, Vol.11 (9), p.1070

2020

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Shear Wave Dispersion in Lean Versus Steatotic Rat Livers

Journal of ultrasound in medicine, 2015-06, Vol.34 (6), p.1123-1129

2015

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Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Frontiers in medicine, 2023-03, Vol.10, p.1058919

2023

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Amygdala nuclei critical for emotional learning exhibit unique gene expression patterns

Neurobiology of learning and memory, 2013-09, Vol.104, p.110-121

2013

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Ensemble transfer learning for the prediction of anti-cancer drug response

Scientific reports, 2020-10, Vol.10 (1), p.18040, Article 18040

2020

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RNA Specificity and Autoregulation of DDX17, a Modulator of MicroRNA Biogenesis

Cell reports (Cambridge), 2019-12, Vol.29 (12)

2019

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Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models

Journal of clinical oncology, 2020-05, Vol.38 (15_suppl), p.e14073-e14073

2020

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Multifactorial drug response modeling based on cancer organoid data

Journal of clinical oncology, 2022-06, Vol.40 (16_suppl), p.e13544-e13544

2022

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A cross-study analysis of drug response prediction in cancer cell lines

Briefings in bioinformatics, 2022-01, Vol.23 (1)

2022

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Cryo-EM Structures of Human Drosha and DGCR8 in Complex with Primary MicroRNA

RNA Specificity and Autoregulation of DDX17, a Modulator of MicroRNA Biogenesis

Heme enables proper positioning of Drosha and DGCR8 on primary microRNAs

Converting tabular data into images for deep learning with convolutional neural networks

Enhancing the Prefusion Conformational Stability of SARS-CoV-2 Spike Protein Through Structure-Guided Design

Geometric parameters that affect the behavior of logic-gated CAR T cells

Learning curves for drug response prediction in cancer cell lines

What Do We Know About Shear Wave Dispersion in Normal and Steatotic Livers?

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Publisher Correction: Heme enables proper positioning of Drosha and DGCR8 on primary microRNAs

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Mouse Liver Dispersion for the Diagnosis of Early-Stage Fatty Liver Disease: A 70-Sample Study

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Publisher Correction: Converting tabular data into images for deep learning with convolutional neural networks

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Protocol for high-throughput cloning, expression, purification, and evaluation of bispecific antibodies

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

Shear Wave Dispersion in Lean Versus Steatotic Rat Livers

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Amygdala nuclei critical for emotional learning exhibit unique gene expression patterns

Ensemble transfer learning for the prediction of anti-cancer drug response

RNA Specificity and Autoregulation of DDX17, a Modulator of MicroRNA Biogenesis

Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models

Multifactorial drug response modeling based on cancer organoid data

A cross-study analysis of drug response prediction in cancer cell lines

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