Table_2_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_2_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_3_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_4_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Table_4_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.XLSX by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Schematic representation of selected data from the Allen Institute dataset. by Saeed Mohammadzadeh (13492285)

The profile log-likelihood of data simulated from four distinct MA(1) models, demonstrating a few examples of multimodal likelihood surfaces. by Jesse Wheeler (18462457)

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: a data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”

In-degree distributions of Boolean functions inferred with SAILoR. by Žiga Pušnik (18806074)

Alignment of amino-acids sequence of nucleotide-binding domains of Ssa1 with DnaK from <i>E</i>. <i>coli</i> using the Needleman-Wunsch algorithm for global alignment. by Deepika Gaur (814560)

Experimentally measured protection from mutations and expected resulting increase in circuit lifetime. by Antoine L. Decrulle (11540003)

S1 Dataset - by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Test results of different training methods. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Schematic diagram of subpixel convolution. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

PS-UNet++ model structure. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Network structure diagram of UNet++. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Sub-pixel convolution upsampling module. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”