Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

2025-01-14 - PAG Talk - Kmerseek - Olga Botvinnik - Seanome by Olga Botvinnik (20571431)

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home by Ying Zhang (40767)

“…However, the labor-intensive sample preparation and nucleic acid amplification steps, along with the complexity and bulkiness of detection equipment, have limited the large-scale application of molecular testing at home. …”

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home by Ying Zhang (40767)

“…However, the labor-intensive sample preparation and nucleic acid amplification steps, along with the complexity and bulkiness of detection equipment, have limited the large-scale application of molecular testing at home. …”

Rapid Detection of Physicochemical Indicators of Tobacco Flavorings Using Fourier-Transform Near Infrared Spectroscopy with Chemometrics and Machine Learning by Qinlin Xiao (14813476)

“…Partial least-squares regression (PLSR), decision tree (DT), least-squares-support vector machine (LSSVM), and convolutional neural network regression (CNNR) were applied to establish detection models. For acid value, the normalization-SPA-LSSVM model achieved the best performance, reaching an R²p of 0.929, RMSEP of 1.155, and an RPD of 3.741. …”

Conserved motif analysis of Human and <i>Leishmania Strumpellin</i> proteins using MEME suite. by Yusr Saadi-Ben Aoun (20469385)

<b>Exploring blood diagnostic markers for diabetic nephropathy through metabolomics and machine learning</b> by yuan sun (20399105)

“…However, there are still many deficiencies in clinical detection. In this article, we aimed to identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by metabolomics studies and machine learning algorithms.…”

Data sharing by Zihan Jin (20638007)

“…Instead of micropumps, miniaturized exciters were employed as SMET drivers to generate multiple excitation waveforms, producing various signal types to improve specific algorithmic accuracy. …”

<b>Exploring blood diagnostic markers for diabetic nephropathy through metabolomics and machine learning</b> by yuan sun (20399105)

“…However, there are still many deficiencies in clinical detection. In this article, we aimed to identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by metabolomics studies and machine learning algorithms.…”

Table 2_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 8_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 4_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 5_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 6_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 7_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Table 3_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Data Sheet 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.docx by Taorui Wang (22300702)

“…Subsequently, machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were applied to construct predictive models. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…Solvents A and B consisted of 0.1% formic acid in water and 80% acetonitrile with 0.1% formic acid, respectively. …”