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 tr
Such a framework enables us to classify cells to unseen Cell Ontology terms based on their distances to other seen terms on the Cell Ontology graph (Fig. 1). OnClass is a Python-based open source package able to compute cell type similarities between the hierarchical structure of existing cell...
neurons of the brain. In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a period of time, as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG, that is, the type of ...
Using MSBase Registry data, we assessed patients for study eligibility based on the following criteria: a diagnosis of relapse-onset MS, European ethnicity, female, Australian, minimum five years of clinical follow-up, minimum three relapse-independent EDSS scores recorded, and available genotype and ...
Convolutional neural network (CNN) A neural network is a computational system that simu- lates neurons of the brain. Every neural network has in- put, hidden, and output layers. Each layer has a structure in which multiple nodes are connected by edges. A "deep neural network" is defined ...
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 ...
[62]. We initialized the network with weights pre-trained on ImageNet [14] and froze the first few layers of the network so that their weights were not updated during backpropagation. We can freeze the pre-set weights for neurons of the top (first) few layers that recognize lines, edges...
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...
A rectified linear unit (ReLU) activation layer followed each batch normalization layer. The choice of the ReLU activation function was based on the non-linear classification task that was required of the input images. Each ReLU layer ensured that neurons with negative values remained inactive, allo...
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. ...