This dataset contains gene expression profiles specifically curated for the development of computational models aimed at early leukemia diagnosis. Each sample represents normalized expression levels of multiple genes derived from microarray experiments conducted on leukemia patients and healthy individuals. The dataset includes three primary diagnostic classes: Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), and Healthy Controls.

The dataset has been carefully preprocessed to ensure data quality:

Missing values have been imputed.

Normalization has been applied to ensure uniform scaling of gene expression values.

It serves as a benchmark resource for researchers aiming to explore feature selection, classification algorithms, and optimization techniques in biomedical data science, particularly for predictive leukemia diagnosis using machine learning.

Csv files containing all detectable genes.

The published dataset consists of four sperate datasets:

• BC-TCGA consists of 17,814 genes and 590 samples (including 61 normal tissue samples and 529 breast cancer tissue samples).
• GSE2034 includes 12,634 genes and 286 breast cancer samples (including 107 recurrence tumor samples and 179 no recurrence samples).
• GSE25066 has 492 breast cancer samples available (including 100 pathologic complete response (PCR) samples and 392 residual disease (RD) samples) and 12,634 genes.
• Simulation Data includes 100 positive samples and 100 negative samples with 10,000 features, and each feature in SData follows normal distributions: N(0, 0.1) and N(0 ± r, 0.1) for positive and negative samples, respectively, where r ∈ [−0.125, 0.125].

All of the datasets are used in the experiments in the paper (Comparison among dimensionality reduction techniques based on Random Projection for cancer classification, Xie et al., 2016).

Bgee is a database for retrieval and comparison of gene expression patterns across multiple animal species. It provides an intuitive answer to the question -where is a gene expressed?- and supports research in cancer and agriculture, as well as evolutionary biology.

The published dataset consists of four sperate datasets:

• BC-TCGA consists of 17,814 genes and 590 samples (including 61 normal tissue samples and 529 breast cancer tissue samples).
• GSE2034 includes 12,634 genes and 286 breast cancer samples (including 107 recurrence tumor samples and 179 no recurrence samples).
• GSE25066 has 492 breast cancer samples available (including 100 pathologic complete response (PCR) samples and 392 residual disease (RD) samples) and 12,634 genes.
• Simulation Data includes 100 positive samples and 100 negative samples with 10,000 features, and each feature in SData follows normal distributions: N(0, 0.1) and N(0 ± r, 0.1) for positive and negative samples, respectively, where r ∈ [−0.125, 0.125].

All of the datasets are used in the experiments in the paper (Comparison among dimensionality reduction techniques based on Random Projection for cancer classification, Xie et al., 2016).

Xie, Haozhe; Li, Jie; Jatkoe, Tim; Hatzis, Christos (2017), “Gene Expression Profiles of Breast Cancer”, Mendeley Data, V1, doi: 10.17632/v3cc2p38hb.1

Gene Expression Omnibus is a public functional genomics data repository supporting MIAME-compliant submissions of array- and sequence-based data. Tools are provided to help users query and download experiments and curated gene expression profiles.

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control. Resources in this dataset:Resource Title: Website Pointer for Plant Expression Database, Iowa State University. File Name: Web Page, url: https://www.bcb.iastate.edu/plant-expression-database [NOTE: PLEXdb is no longer available online. Oct 2019.] Project description for the Plant Expression Database (PLEXdb) and integrated tools.

Gene expression profiles for tissues from GTEx by RNA-seq

Breast Cancer Gene Expression Dataset

This dataset contains RNA-seq gene expression data from 58 breast cancer patients treated with neoadjuvant chemotherapy (NAC). The data is derived from GSE280902 on NCBI GEO.

Files

• cleaned_expression.csv: Gene expression matrix with 58 samples (rows) and 28,278 genes (columns). The last column is 'Response' (1 for responder, 0 for non-responder).
• labels.csv: Sample labels with response to NAC.

Data Description

• Samples: 58 breast cancer patients (29 responders, 29 non-responders to NAC).
• Genes: 28,278 protein-coding genes.
• Response: 1 = Pathological Complete Response (pCR), 0 = No Response.

Source

• GEO Accession: GSE280902
• Paper: Guevara-Nieto HM et al. Identification of predictive pretreatment biomarkers for neoadjuvant chemotherapy response in Latino invasive breast cancer patients. Mol Med 2025.
• GitHub Repository: Breast Cancer Gene Expression Processed Data

Usage

This dataset can be used for machine learning models to predict NAC response in breast cancer based on gene expression profiles.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Gene expression data from Gene Expression Omnibus (GEO) database.

Accumulating data support the concept that ionizing radiation therapy (RT) has the potential to convert the tumor into an in situ individualized vaccine; however this potential is rarely realized by RT alone. Transforming growth factor xce xb2 (TGF xce xb2) is an immunosuppressive cytokine that is activated by RT and inhibits the antigen-presenting function of dendritic cells and the differentiation of effector CD8+ T cells. Here we tested the hypothesis that TGF xce xb2 hinders the ability of RT to promote anti-tumor immunity. Development of tumor-specific immunity was examined in a pre-clinical model of metastatic breast cancer. Mice bearing established 4T1 mouse mammary carcinoma treated with pan-isoform specific TGF xce xb2 neutralizing antibody 1D11 showed significantly improved control of the irradiated tumor and non-irradiated metastases but no effect in the absence of RT. Notably whole tumor transcriptional analysis demonstrated the selective upregulation of genes associated with immune-mediated rejection only in tumors of mice treated with RT+TGF xce xb2 blockade. Mice treated with RT+TGF xce xb2 blockade exhibited cross-priming of CD8+ T cells producing IFN xce xb3 in response to three tumor-specific antigens in tumor-draining lymph nodes which was not evident for single modality treatment. Analysis of the immune infiltrate in mouse tumors showed a significant increase in CD4+ and CD8+ T cells only in mice treated with the combination of RT+TGF xce xb2 blockade. Depletion of CD4+ or CD8+ T cells abrogated the therapeutic benefit of RT+TGF xce xb2 blockade. These data identify TGF xce xb2 as a master inhibitor of the ability of RT to generate an in situ tumor vaccine which supports testing inhibition of TGF xce xb2 during radiotherapy to promote therapeutically effective anti-tumor immunity. We used genome-wide microarray to depict main biological processes responsibles for the therapeutic benefit of the combination ofTGF-beta blockade and local radiotherapy. To gain a more comprehensice protrait of the effects of RT and TGFbeta blockade on gene expressionin tumors we collected 4T1 tumors 4 days after completion of RT. Three tumors from each group were then subjected to RNA extraction and hybridization on affymetrix array.

This data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on sequence based RNA levels in human tissue and cell line.

GEO (Gene Expression Omnibus) is a public functional genomics data repository supporting MIAME-compliant data submissions. There are also tools provided to help users query and download experiments and curated gene expression profiles.

Premise of the study: The root apex is an important region involved in environmental sensing, but comprises a very small part of the root. Obtaining root apex transcriptomes is therefore challenging when the samples are limited. The feasibility of using tiny root sections for transcriptome analysis was examined, comparing RNA sequencing (RNA-Seq) to microarrays in characterizing genes that are relevant to spaceflight.Methods:Arabidopsis thaliana Columbia ecotype (Col-0) roots were sectioned into Zone 1 (0.5 mm; root cap and meristematic zone) and Zone 2 (1.5 mm; transition, elongation, and growth-terminating zone). Differential gene expression in each was compared.Results: Both microarrays and RNA-Seq proved applicable to the small samples. A total of 4180 genes were differentially expressed (with fold changes of 2 or greater) between Zone 1 and Zone 2. In addition, 771 unique genes and 19 novel transcriptionally active regions were identified by RNA-Seq that were not detected in microarrays. However, microarrays detected spaceflight-relevant genes that were missed in RNA-Seq. Discussion: Single root tip subsections can be used for transcriptome analysis using either RNA-Seq or microarrays. Both RNA-Seq and microarrays provided novel information. These data suggest that techniques for dealing with small, rare samples from spaceflight can be further enhanced, and that RNA-Seq may miss some spaceflight-relevant changes in gene expression.

This collection of data is part of the RNA-Seq (HiSeq) PANCAN dataset. It is a random extraction of gene expressions of patients having different types of tumor: BRCA, KIRC, COAD, LUAD, and PRAD. Each sample contains the expression of 20,531 genes for a patient diagnosed with one of the following cancers:

Files:

• data.csv: Gene expression matrix X (881 samples × 20,531 genes)
• label.csv: True class label for each sample y (881 labels)

Source: UCI ML Repository – Gene Expression Cancer RNA-Seq Data

Microgravity exposure as well as chronic muscle disuse are two of the main causes of physiological adaptive skeletal muscle atrophy in humans and murine animals in physiological condition. The aim of this study was to investigate at both morphological and global gene expression level skeletal muscle adaptation to microgravity in mouse soleus and extensor digitorum longus (EDL). Adult male mice C57BL/N6 were flown aboard the BION-M1 biosatellite for 30 days on orbit (BF) or housed in a replicate flight habitat on Earth (BG) as reference flight control. In this study we investigated for the first time gene expression adaptation to 30 days of microgravity exposure in mouse soleus and EDL highlighting potential new targets for improvement of countermeasures able to ameliorate or even prevent microgravity-induced atrophy in future spaceflights. Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.

This template is for recording gene expression data from the NimbleGen platform. This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

14 gene expression datasets used in this study.

🧬 RNA-seq Gene Expression Data for Predicting Neoadjuvant Chemotherapy Response

  Overview

The Breast Cancer Gene Expression Dataset contains RNA-seq gene expression profiles from 58 breast cancer patients treated with neoadjuvant chemotherapy (NAC).The dataset is processed and cleaned from the publicly available NCBI GEO dataset GSE280902 and is designed for machine learning, bioinformatics, and translational cancer research. The primary goal of this dataset is to support… See the full description on the dataset page: https://huggingface.co/datasets/mubashir1837/Breast-Cancer-Gen-Expression-Dataset.

mRNA microarray expression profiles for cancer cell lines