Data and metadata supporting the published article: Upregulation of lipid metabolism genes in the breast prior to cancer diagnosis by Natascia Marino (9232357)

“…</p><p>The following techniques are described in more detail in the published article: breast tissue microdissection and RNA extraction, whole transcriptome sequencing, data analysis, quantitative real time polymerase chain reaction (qPCR), immunohistochemistry, primary breast epithelial cells: cultures and immunofluorescence, and statistical analysis.…”

Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

Changes in GABA neurons (RiboTag::Gad2-Cre). by Lech Kaczmarczyk (587512)

Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity. by Nantia Leonidou (14833068)

Changes in glutamatergic neurons (RiboTag::vGluT2-Cre). by Lech Kaczmarczyk (587512)

Changes in astrocytes (RiboTag::Cx43-Cre). by Lech Kaczmarczyk (587512)

Maternal blood <i>EBF1</i>-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: a nested case-control study by Guoli Zhou (225819)

“…<p>Both genetic variants and maternal blood mRNA levels of <i>EBF1</i> gene have been linked to sPTB. Animal and human studies suggest that specific <i>EBF1</i>-based miRNAs are involved in various physiological and pathophysiological processes. …”

The evolution of gene-specific expression noise was simulated using populations of model gene regulatory networks with mutable levels of gene-specific expression noise under select... by Nataša Puzović (7866440)

“…If the populations are evolved under selection, fitness is calculated as the distance of the expression level of each gene from the optimal expression level. Genotypes are reproduced based on their relative fitness and mutations in the intrinsic noise vectors are introduced. …”

Data Sheet 1_AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae.docx by Mary-Francis LaPorte (20821568)

“…One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. …”

Streamlining signaling pathway reconstruction presentation by Chris Magnano (10760405)

“…Each individual method has its own input and output file formats, installation process, and user-specified parameters. Different algorithms employ varied objective functions and optimization strategies, and recognizing which method is appropriate for a particular dataset and how to set its unique parameters requires domain expertise in pathway reconstruction. …”

Table_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.xlsx by Yao Gao (1420339)

“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”

Image_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.tif by Yao Gao (1420339)

“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”

Intelligent Healthcare Systems by Vania Vieira Estrela (9084371)

Subjects:

Data Sheet 1_A novel lactylation-related gene signature to predict prognosis and treatment response in lung adenocarcinoma.docx by Hongyi Zhang (431841)

“…Additionally, various algorithms were used to explore the relationship between the risk score and immune infiltration levels, with model genes analyzed based on single-cell sequencing. …”

Table_3_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Image_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Image_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Table_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Data_Sheet_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.docx by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Data_Sheet_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.CSV by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”