Predicting Tissue of Origin from Bulk Tumor Gene Expression using a Pre-trained Transformer Model
Mellors, T.; Spitmann, M.
Show abstract
Identifying the tissue of origin for cancers is essential for enhancing diagnostic precision, selecting effective treatments, and guiding clinical decision-making. In this study, we developed a predictive model to classify the tissue of origin across various cancer types. Using a dataset with 10,300 samples from 32 unique tissue types, the model achieved an overall accuracy of 88% in distinguishing among all 32 classes, with an average accuracy of 99.2% within each class. When tested on metastatic skin tumors, it reached an accuracy of 87%, underscoring its potential in addressing challenging metastatic cases. These results demonstrate the models reliability in oncology applications, offering a promising tool for improving diagnostic accuracy and supporting personalized cancer treatment strategies.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification 94%
- Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images 93%
- Wide and Deep Learning for Automatic Cell Type Identification 93%
Similar papers in this journal
- Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors 92%
- Using Adversarial Images to Assess the Stability of Deep Learning Models Trained on Diagnostic Images in Oncology 91%
- Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma 90%
Similar papers in this journal
- Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment 93%
- PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence, A Case Study for Prostate Cancer Research 92%
- Accurate Prediction of Breast Cancer Survival through Coherent Voting Networks with Gene Expression Profiling 92%
Similar papers in this journal
Similar papers in this journal
- BC-Predict: Mining of signal biomarkers and multilevel validation of cascade classifier for early-stage breast cancer subtyping and prognosis 92%
- Reliable machine learning models in genomic medicine using conformal prediction 91%
- Predicting GD2 expression across cancer types by the integration of pathway topology and transcriptome data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.