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Application of non-mydriatic fundus examination and artificial intelligence to promote the screening of diabetic retinopathy in the endocrine clinic: an observational study of T2DM patients in Tianjin, China.
In Therapeutic advances in chronic disease
Hao Zhaohu, Cui Shanshan, Zhu Yanjuan, Shao Hailin, Huang Xiao, Jiang Xia, Xu Rong, Chang Baocheng, Li Huanming
artificial intelligence, diabetic retinopathy, fundus screening, non-mydratic fundus examination
Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems.
In Frontiers in genetics ; h5-index 62.0
Bresolin Tiago, Dórea João R R
beef cattle, dairy cattle, mid-infrared, near-infrared, novel phenotypes, spectral information
The Molecular Basis of JAZ-MYC Coupling, a Protein-Protein Interface Essential for Plant Response to Stressors.
In Frontiers in plant science
Oña Chuquimarca Samara, Ayala-Ruano Sebastián, Goossens Jonas, Pauwels Laurens, Goossens Alain, Leon-Reyes Antonio, Ángel Méndez Miguel
JAZ, MYC, computer, hotspots, machine learning, modeling, plant defense
Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data.
In Frontiers in psychology ; h5-index 92.0
Levy Jessica, Mussack Dominic, Brunner Martin, Keller Ulrich, Cardoso-Leite Pedro, Fischbach Antoine
longitudinal data, machine learning, model comparison, school effectiveness, value-added modeling
A Four-Step Method for the Development of an ADHD-VR Digital Game Diagnostic Tool Prototype for Children Using a DL Model.
In Frontiers in psychiatry
Wiguna Tjhin, Wigantara Ngurah Agung, Ismail Raden Irawati, Kaligis Fransiska, Minayati Kusuma, Bahana Raymond, Dirgantoro Bayu
Indonesia, attention-deficit/hyperactivity disorder, diagnostic tool, digital game, machine learning, neuropsychological test, virtual reality
Erratum: Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders.
In Frontiers in pharmacology
Shayakhmetov Rim, Kuznetsov Maksim, Zhebrak Alexander, Kadurin Artur, Nikolenko Sergey, Aliper Alexander, Polykovskiy Daniil
adversarial autoencoders, conditional generation, deep learning, drug discovery, gene expression, generative models, representation learning