Table 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xls by Haoxue Zhang (12208580)

“…</p>Methods<p>Transcriptomic data from TCGA-SKCM and five GEO datasets were analyzed. Ten machine learning algorithms were integrated to build 101 prognostic models. …”

Image 4_Using baseline MRI radiomics to predict the tumor shrinkage patterns in HR-Positive, HER2-Negative Breast Cancer.jpg by Lijia Wang (314011)

“…Radiomics features were extracted and analyzed using machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). …”

Image 2_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.tif by Yanan Li (94033)

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”

DataSheet2_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF by D. S. Bug (19791612)

“…The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. …”

Methods for nonlinear, non-Gaussian, and data-driven ensemble data assimilation in large-scale applications by Ian Grooms (8740770)

“…Machine learning methods have been developed to reduce the cost of large ensemble forecasts by reducing the number of costly physics-based forecasts to just one, or a small number, followed by the use of generative models to create a large ensemble of synthetic analogs of the physics-based forecasts. …”

The proposed framework. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Table 3_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…</p>Methods<p>Transcriptomic data from TCGA-SKCM and five GEO datasets were analyzed. Ten machine learning algorithms were integrated to build 101 prognostic models. …”

Number of recognised words. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

DataSheet2_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF by D. S. Bug (19791612)

“…The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. …”

Table 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…</p>Methods<p>Transcriptomic data from TCGA-SKCM and five GEO datasets were analyzed. Ten machine learning algorithms were integrated to build 101 prognostic models. …”

Data Sheet 1_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.docx by Yanan Li (94033)

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”

Data Sheet 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…</p>Methods<p>Transcriptomic data from TCGA-SKCM and five GEO datasets were analyzed. Ten machine learning algorithms were integrated to build 101 prognostic models. …”

Table 2_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.xls by Yanan Li (94033)

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”

Data Sheet 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…</p>Methods<p>Transcriptomic data from TCGA-SKCM and five GEO datasets were analyzed. Ten machine learning algorithms were integrated to build 101 prognostic models. …”

Word2Vec models [3]. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

DataSheet1_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF by D. S. Bug (19791612)

“…The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. …”

Image 1_Using baseline MRI radiomics to predict the tumor shrinkage patterns in HR-Positive, HER2-Negative Breast Cancer.jpg by Lijia Wang (314011)

“…Radiomics features were extracted and analyzed using machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). …”

Clustering of named entities AraVec model. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Table 1_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.xlsx by Yanan Li (94033)

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”

DataSheet1_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF by D. S. Bug (19791612)

“…The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. …”