Risk Groups Immune Infiltration Analysis by ssGSEA Algorithm. by Yongling Wang (13261656)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

An example of the algorithm of the ORFanID engine is explained in a UML flow chart diagram. by Richard S. Gunasekera (6752072)

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Table2_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Table3_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Table4_Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.XLSX by Lei Chen (54296)

“…As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.…”

Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. by Junyi Zhou (495644)

DataSheet_1_Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.docx by Hang Zhou (230977)

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Differences between average F1 obtained for PFP-WGAN and DeepGO-Seq for the GO terms in each height ( and ). by Seyyede Fatemeh Seyyedsalehi (10196767)

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F1 obtained for PFP-WGAN and DeepGO-Seq for GO terms as a function of number of available positive training samples. by Seyyede Fatemeh Seyyedsalehi (10196767)

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The results of sensitivity analysis on the developed ensemble model to rank the importance of factors involved in <i>Agrobacterium</i>-mediated gene transformation of chrysanthemum... by Mohsen Hesami (6917390)

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Four-bit one-hot encoding of the RNA sequence. by Zhongzhong Guo (12133402)

Subjects: “…differentially expressed genes…”

Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes. by Wenjun Zhao (644283)

“…<p>Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes.…”

V-shaped piecewise linear function. by Žiga Pušnik (18806074)

Functional pathways and evaluation of mutation between the two MRG-related risk groups. by Xuefen Lin (17785367)

Table S1 from The Landscape of Isoform Switches in Human Cancers by Kristoffer Vitting-Seerup (2696728)

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Observed adjusted Rand index x100 between the methods for the partitions found setting <i>K</i> = 10 clusters in the data set with mean gene expressions in two days. by Susana Conde (5495675)

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Observed adjusted Rand index x100 between the methods for the partitions found setting <i>K</i> = 10 clusters in the data set with mean gene expressions in two days. by Susana Conde (5495675)

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