Diminishing Batch Normalization

Ma, Yintai; Klabjan, Diego

doi:10.1109/tnnls.2022.3210840

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…It was beneficial to feature selection to prevent the occurrence of a particularly large neuron weight to reduce overfitting. At the same time, a Batch normalization layer (BN) 36 was added between the full connection layer and the activation function, and the data was divided into small batches for random gradient descent. During the forward transmission, each layer was standardized so that the output value fell into the sensitive area of the input value, and finally, the input of each layer of the neural network keeps the same distribution.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

Chen,

Zhang,

Zhang

et al. 2023

J. Chem. Inf. Model.

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Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.

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Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

Chen,

Zhang,

Zhang

et al. 2023

J. Chem. Inf. Model.

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PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images

Qiu,

Zhou,

Bai

et al. 2024

Med Biol Eng Comput

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The accurate selection of the ultrasound plane for the fetal head and pubic symphysis is critical for precisely measuring the angle of progression. The traditional method depends heavily on sonographers manually selecting the imaging plane. This process is not only time-intensive and laborious but also prone to variability based on the clinicians’ expertise. Consequently, there is a significant need for an automated method driven by artificial intelligence. To enhance the efficiency and accuracy of identifying the pubic symphysis-fetal head standard plane (PSFHSP), we proposed a streamlined neural network, PSFHSP-Net, based on a modified version of ResNet-18. This network comprises a single convolutional layer and three residual blocks designed to mitigate noise interference and bolster feature extraction capabilities. The model’s adaptability was further refined by expanding the shared feature layer into task-specific layers. We assessed its performance against both traditional heavyweight and other lightweight models by evaluating metrics such as F1-score, accuracy (ACC), recall, precision, area under the ROC curve (AUC), model parameter count, and frames per second (FPS). The PSFHSP-Net recorded an ACC of 0.8995, an F1-score of 0.9075, a recall of 0.9191, and a precision of 0.9022. This model surpassed other heavyweight and lightweight models in these metrics. Notably, it featured the smallest model size (1.48 MB) and the highest processing speed (65.7909 FPS), meeting the real-time processing criterion of over 24 images per second. While the AUC of our model was 0.930, slightly lower than that of ResNet34 (0.935), it showed a marked improvement over ResNet-18 in testing, with increases in ACC and F1-score of 0.0435 and 0.0306, respectively. However, precision saw a slight decrease from 0.9184 to 0.9022, a reduction of 0.0162. Despite these trade-offs, the compression of the model significantly reduced its size from 42.64 to 1.48 MB and increased its inference speed by 4.4753 to 65.7909 FPS. The results confirm that the PSFHSP-Net is capable of swiftly and effectively identifying the PSFHSP, thereby facilitating accurate measurements of the angle of progression. This development represents a significant advancement in automating fetal imaging analysis, promising enhanced consistency and reduced operator dependency in clinical settings. Graphical abstract

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TCNet: Triple Collocation-Based Network for Ocean Surface Wind Speed Retrieval on CYGNSS

Shi,

Su,

Wang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Diminishing Batch Normalization

Cited by 7 publications

References 16 publications

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images

TCNet: Triple Collocation-Based Network for Ocean Surface Wind Speed Retrieval on CYGNSS

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