GA parameters and objective function. by André G. Coimbra (21354135)

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

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. 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)

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. 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)

“…Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. 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.…”

Supplementary Files 1-11 from Protein Signatures of NK Cell–Mediated Melanoma Killing Predict Response to Immunotherapies by Sabrina Cappello (14950324)

Hyper parameter settings for ML Algorithms. by R. S. Jayaram (22139155)

The recurrent T3D algorithm. by Neythen J. Treloar (8698251)

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

State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations by Runduo Liu (10756379)

Subjects:

Filip_DataHazards_Poster.pdf by Natalie Zelenka (14852105)

Subjects:

Functional enrichment analysis of DEGs. by Huiling Deng (4427161)

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

Parameters used to train the xgboost final models through the extreme gradient boosting algorithm in this work (n = 11,009 species). by Luis Javier Madrigal-Roca (17743035)

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

Hierarchical communities obtained from CoV-human network using the “community-based” visualization option provided by UNIPred-Web. by Paolo Perlasca (9909695)

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Fig 7 - by Paolo Perlasca (9909695)

Subjects:

Functional and stability predictions of SOD1 protein variants. by Gabriel Rodrigues Coutinho Pereira (6612542)

“…<p>The SOD1 variants compiled from the literature and databases (233) were analyzed using functional and stability prediction algorithms. …”

Functional enrichment analysis. by Junjie Su (368807)

Subjects:

Table_1_Random forest algorithm for predicting postoperative delirium in older patients.DOCX by Weixuan Sheng (17772804)

“…Objective<p>In this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients.…”

Image_1_Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis.tif by Wandong Hong (6683141)