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Shraddha Thakkar

Showing results (1-10 of 45) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|October 9, 2015
Decellularized Extracellular Matrix Scaffolds for Cartilage RegenerationShraddha Thakkar, Hugo Fernandes, Lorenzo Moroni
The AAPS Journal|April 27, 2016
The FDA's Experience with Emerging Genomics Technologies-Past, Present, and FutureJoshua Xu, Shraddha Thakkar, Binsheng Gong, et al.
Scientific Reports|December 13, 2017
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved DrugsHuixiao Hong, Shraddha Thakkar, Minjun Chen, et al.
Bioorganic & Medicinal Chemistry Letters|May 30, 2015
Heteroaromatic analogs of the resveratrol analog DMU-212 as potent anti-cancer agentsNarsimha Reddy Penthala, Shraddha Thakkar, Peter A Crooks
Chemical Research in Toxicology|December 7, 2019
Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?Zhichao Liu, Liyuan Zhu, Shraddha Thakkar, et al.
Frontiers in Artificial Intelligence|December 6, 2021
DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level RepresentationTing Li, Weida Tong, Ruth Roberts, et al.
Regulatory Toxicology and Pharmacology : RTP|August 26, 2023
DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory applicationTing Li, Zhichao Liu, Shraddha Thakkar, et al.
Frontiers in Pharmacology|April 28, 2022
Editorial: Emerging Technologies Powering Rare and Neglected Disease Diagnosis and Theraphy DevelopmentZhichao Liu, Qais Hatim, Shraddha Thakkar, et al.
Frontiers in Artificial Intelligence|December 15, 2022
Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representationTing Li, Weida Tong, Ruth Roberts, et al.
Frontiers in Bioengineering and Biotechnology|December 17, 2020
Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver InjuryTing Li, Weida Tong, Ruth Roberts, et al.
Pageof 5

Showing results (1-10 of 45) with videos related to

Sort By:
Pageof 5
Methods in Molecular Biology (Clifton, N.J.)|October 9, 2015
Decellularized Extracellular Matrix Scaffolds for Cartilage RegenerationShraddha Thakkar, Hugo Fernandes, Lorenzo Moroni
The AAPS Journal|April 27, 2016
The FDA's Experience with Emerging Genomics Technologies-Past, Present, and FutureJoshua Xu, Shraddha Thakkar, Binsheng Gong, et al.
Scientific Reports|December 13, 2017
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved DrugsHuixiao Hong, Shraddha Thakkar, Minjun Chen, et al.
Bioorganic & Medicinal Chemistry Letters|May 30, 2015
Heteroaromatic analogs of the resveratrol analog DMU-212 as potent anti-cancer agentsNarsimha Reddy Penthala, Shraddha Thakkar, Peter A Crooks
Chemical Research in Toxicology|December 7, 2019
Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?Zhichao Liu, Liyuan Zhu, Shraddha Thakkar, et al.
Frontiers in Artificial Intelligence|December 6, 2021
DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level RepresentationTing Li, Weida Tong, Ruth Roberts, et al.
Regulatory Toxicology and Pharmacology : RTP|August 26, 2023
DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory applicationTing Li, Zhichao Liu, Shraddha Thakkar, et al.
Frontiers in Pharmacology|April 28, 2022
Editorial: Emerging Technologies Powering Rare and Neglected Disease Diagnosis and Theraphy DevelopmentZhichao Liu, Qais Hatim, Shraddha Thakkar, et al.
Frontiers in Artificial Intelligence|December 15, 2022
Corrigendum: DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representationTing Li, Weida Tong, Ruth Roberts, et al.
Frontiers in Bioengineering and Biotechnology|December 17, 2020
Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver InjuryTing Li, Weida Tong, Ruth Roberts, et al.
Pageof 5