Table 1_Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3.xlsx by Ziqiang Wang (1628740)

“…Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. …”

Data Sheet 1_Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3.... by Ziqiang Wang (1628740)

“…Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. …”

Image 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.tif by Ignacio Boira (21562568)

“…Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Table 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.docx by Ignacio Boira (21562568)

“…Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Image 4_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”

Image 3_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”

Image 5_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”

Image 1_Decoding monocyte heterogeneity in sepsis: a single-cell apoptotic signature for immune stratification and guiding precision therapy.tif by Wenjuan Duan (11875262)

“…A machine learning pipeline incorporating SVM, RF, XGB, and GLM algorithms was used to identify hub genes associated with monocyte apoptosis. …”

Image 2_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”

Image 1_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”

Predictive model-building process. by Mukhtar Ijaiya (18935122)

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”

Table 2_A real−world pharmacovigilance study of FDA Adverse Event Reporting System events for pralsetinib.xlsx by Yi Yin (448434)

“…A total of 95 significant disproportionality preferred terms (PTs) conformed to the four algorithms simultaneously. AEs that ranked the top three at the PT level were hypertension (n = 80), asthenia (n = 79), and anemia (n = 65). …”

Table 3_A real−world pharmacovigilance study of FDA Adverse Event Reporting System events for pralsetinib.xlsx by Yi Yin (448434)

“…A total of 95 significant disproportionality preferred terms (PTs) conformed to the four algorithms simultaneously. AEs that ranked the top three at the PT level were hypertension (n = 80), asthenia (n = 79), and anemia (n = 65). …”

List of data tables. by Mukhtar Ijaiya (18935122)

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”

Flow chart of data source inclusion. by Mukhtar Ijaiya (18935122)

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”

Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”

Table 1_A real−world pharmacovigilance study of FDA Adverse Event Reporting System events for pralsetinib.docx by Yi Yin (448434)

“…A total of 95 significant disproportionality preferred terms (PTs) conformed to the four algorithms simultaneously. AEs that ranked the top three at the PT level were hypertension (n = 80), asthenia (n = 79), and anemia (n = 65). …”

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.…”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…<br>Rather than attempting neuron-level interpretability, Genosophus treats a neural network as a <b>high-dimensional dynamical system</b> and measures how its embedding geometry evolves across time or across model states.…”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p dir="ltr">- `canonical_prefix`: cleaned/standardized prefix used for constructing `sample_id` </p><p dir="ltr"> - `NOR`, `HBP`, `DIA`, `HD`, `CRC`, `LUN`, `SPAN`. …”