Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions
RNA-seq (gene expression) · human · 215 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 215 samples: 31 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesionsGenome medicine · 2026
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE320389_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1427714. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 137 samples.
How we read this: 31 replicated groups read from 215 sample titles; they account for 137 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM9056556 4202-002-67
- GSM9056557 4202-003-67
- GSM9056558 4202-004-67
- GSM9056559 4202-006-67
- GSM9056560 4202-011-67
- GSM9056561 4202-012-67
- GSM9056562 4202-013-67
- GSM9056563 4202-030-67
- GSM9056564 4202-033-67
- GSM9056565 4438-002-69
- GSM9056566 4438-003-69
- GSM9056567 4438-005-67
- GSM9056568 4438-007-67
- GSM9056569 4438-011-67
- GSM9056570 4438-026-67
- GSM9056571 4438-032-67
- GSM9056572 4438-042-67
- GSM9056573 4438-082-67
- GSM9056574 4714-001-67
- GSM9056575 4714-018-67
- GSM9056576 4714-020-67
- GSM9056577 4790-002-67
- GSM9056578 4790-003-67
- GSM9056579 4790-004-67
- GSM9056580 4790-005-67
- GSM9056581 4790-011-67
- GSM9056582 4790-012-67
- GSM9056583 4790-013-67
- GSM9056584 4790-014-67
- GSM9056585 4790-015-67
- GSM9056586 4790-016-67
- GSM9056587 4790-021-67
- GSM9056588 4790-022-67
- GSM9056589 4790-026-67
- GSM9056590 4790-034-67
- GSM9056591 4791-002-69
- GSM9056592 4791-007-67
- GSM9056593 4791-011-67
- GSM9056594 4793-030-67
- GSM9056595 4793-038-67
+ 175 more — browse all 215 samples with per-sample file links →
- GSE320381 Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions [HTAN cohort] 145 samples shares attention-based, premalignant, pathology, learning, squamous, lesions, images, deep
- GSE300258 Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions [DECAMP cohort] 70 samples shares attention-based, premalignant, pathology, learning, squamous, lesions, images, deep
- GSE319666 Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease 177 samples shares premalignant, lesions
- GSE196711 Immune profiling of premalignant lesions in patients with Peutz-Jeghers Syndrome 29 samples shares premalignant, lesions
- GSE291867 Gene expression profile Predictor on chemical Structures (GPS): Deep Learning-based platform to screen and design novel therapeutics 27 samples shares learning, deep
- GSE256142 Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning 18 samples shares learning, deep
- GSE134520 Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics] 13 samples shares premalignant, lesions
- GSE299799 A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples 12 samples shares learning, deep
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.