OnClass will then perform the third step to extend these scores to unseen cell types based on the Cell Ontology graph. Since the user-selected approach does not need the ability to classify cells into unseen cell types nor reject cells to all seen cell types, users can thus select their ...
The transgenic line composition of each terminal cluster inGLIF4(Fig.6) suggests that clustering based on model parameters broadly segregates neurons into previously identified classes of cells. Neurons from transgenic lines labeling predominantly excitatory neurons cluster separately from those labeling main...
We performed an extended analysis by including a structure-based investigation on one of our proposed predictions. We provide here a brief description of the structure-based methods were employed in this study and discuss the purpose and rationale of the investigation in the later section of the m...
As the network trains on a sequence of images, the features are acquired. CNNs learn to recognize features by switching between tens or hundreds of hidden layers. This is a multi-layer neural network composed of neurons with trainable weights and biases [9] and it is made possible by ...
For the MLP algorithm, MLPClassifier with 2 hidden layers, 100 neurons in the hidden layer, using Adam optimization algorithm [43] and ReLU activation function [44] was used. The learning rate was 0.001, the momentum coefficient was 0.8, and the epoch number was 200. ...
Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resoluti...
The classification of drones based on their acoustic characteristics is a good way, as drones have very distinguishable acoustic characteristics compared to a similar object like an airplane, birds, helicopters, etc. However, it is important to acknowledge that this method may face challenges in envi...
After building a base model, an ablation study is performed in order to achieve the optimal model configuration, based on performance. This is achieved by changing different components, such as the activation function, hyperparameters, the loss function, and the flatten layer. The training approach...
We evaluated the general performance of the models by the prediction accuracy, precision, and F1-score, which can be obtained from a confusion matrix based on their phases [50]. A confusion matrix allows the visualization of the performance of an algorithm. Based on the actual and predicted ...