Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

Feature selection results. by Doaa Sami Khafaga (21463870)

ANOVA test result. by Doaa Sami Khafaga (21463870)

Summary of literature review. by Doaa Sami Khafaga (21463870)

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

Supplemental Tables S1 and S2 for Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks by Diana Murray (16859046)

“…Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail. …”

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

Table1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.XLSX by Zhen Shen (393133)

“…For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression.</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…”

DataSheet1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.PDF by Zhen Shen (393133)

“…For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression.</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…”

Relative azimuth calculations reveal nanoscale cadherin geometry in the desmosome. by William F. Dean (19370322)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph-CNNs trained gene expression data. by Halima Alachram (11570633)

Accessibility of translation initiation sites is the strongest predictor of heterologous protein expression in <i>E. coli</i>. by Bikash K. Bhandari (11524776)