Table_1_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx by Cheng Yan (406565)

“…Background<p>The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. …”

Table_9_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx by Cheng Yan (406565)

“…Background<p>The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. …”

Table_7_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx by Cheng Yan (406565)

“…Background<p>The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. …”

Table_6_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx by Cheng Yan (406565)

“…Background<p>The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. …”

Image_2_A cellular senescence-related classifier based on a tumorigenesis- and immune infiltration-guided strategy can predict prognosis, immunotherapy response, and candidate drug... by Yi Luo (143206)

“…</p>Methods<p>Tumorigenic and immune infiltration-associated senescence genes were determined by weighted gene co-expression network analysis (WGCNA) and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and subsequently, a prognostic scoring model (named TIS) was constructed using multiple survival analysis algorithms to classify the senescence-related subtypes of HCC. …”

Identification of the Integrated Prognostic Signature Associated with Immuno-relevant Genes and Long Non-coding RNAs in Acute Myeloid Leukemia by Chunxia Zhao (305710)

“…ESTIMATE and CIBERSORT algorithms were applied to investigate the potential impact of infiltrating immune cells based on the obtained signature in tumor microenvironment. …”

Skeletal_ Muscle_MRI_Registration by Lucia Fontana (9435020)

“…</p> <p>wxPython library was employed to develop the GUI, which is composed by two main windows – initial window and registration window – and 5 secondary frames for support functionalities. 3D images are presented with three views – axial, coronal and sagittal – with three sliders to adjust maximum value, minimum value, and gamma correction.…”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18401358)

“…</p><p dir="ltr">The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: <a href="https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset" target="_blank">https://ieeexplore.ieee.org/document/10218394/algorithms?…”

Supporting data for Histone crotonylation is a novel epigenetic regulation and a therapeutic vulnerability for liver cancer treatment by Qidong Li (9069932)

“…Using the ChromHMM machine learning algorithm, we annotated chromatin states based on distinct combinations of these markers. …”

Table2_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table3_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table4_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table1_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table6_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table5_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table_2_Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma.xlsx by Mei Chen (197360)

“…Finally, weighted gene co-expression network analysis was used in identifying the key PMRGs closely related to ccRCC, and cell function experiments were carried out.…”

Table 12_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Image_4_Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma.tif by Mei Chen (197360)

“…Finally, weighted gene co-expression network analysis was used in identifying the key PMRGs closely related to ccRCC, and cell function experiments were carried out.…”

Table 9_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table_1_Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer.DOCX by Kaiyou Fu (10268729)

“…</p>Method<p>Candidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. …”