Yinxi Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yinxi Wang has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Johan Hartman
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
22 shared publications - Balazs Acs
Department of Clinical Pathology and Cancer Diagnostics Karolinska University Hospital Stockholm Sweden
21 shared publications Christer Larsson
21 shared publications- Kimmo Kartasalo
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
21 shared publications Masi Valkonen
21 shared publications- Pekka Ruusuvuori
Institute of Biomedicine, University of Turku, Turku, Finland
21 shared publications
Latest Publications
- Open Access
- 24 June 2023
- Published by Elsevier in European Journal of Cancer
- Vol. 191, 112953
Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information
- 24 June 2023
- Published by Elsevier in European Journal of Cancer
- Vol. 191, 112953
Open AccessTranscriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Supplementary Tables from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Online-only supplementary tables: Table S1. Clinicopathological characteristics of patients per cohort. Table S2. ST gene panel. Table S3. The list of features used in the neural network based classification. Table S4. Results of pre-study hyperparameter tunning on 5 genes. Table S5. Prediction performance for the 1011 genes on validation and test sets. Table S6. Prediction performance for transcripts from established biomarker panels. Table S7. Correlation of gene expression with ER status (internal test set). Table S8. Correlation of gene expression with ER status (external test set). Table S9. Enriched pathways for genes cannot be predicted (Reactome gene sets). Table S10. Enriched pathways for genes cannot be predicted (Hallmark gene sets). Table S11. List of proliferation genes.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Tables from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Online-only supplementary tables: Table S1. Clinicopathological characteristics of patients per cohort. Table S2. ST gene panel. Table S3. The list of features used in the neural network based classification. Table S4. Results of pre-study hyperparameter tunning on 5 genes. Table S5. Prediction performance for the 1011 genes on validation and test sets. Table S6. Prediction performance for transcripts from established biomarker panels. Table S7. Correlation of gene expression with ER status (internal test set). Table S8. Correlation of gene expression with ER status (external test set). Table S9. Enriched pathways for genes cannot be predicted (Reactome gene sets). Table S10. Enriched pathways for genes cannot be predicted (Hallmark gene sets). Table S11. List of proliferation genes.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Supplementary Tables from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Online-only supplementary tables: Table S1. Clinicopathological characteristics of patients per cohort. Table S2. ST gene panel. Table S3. The list of features used in the neural network based classification. Table S4. Results of pre-study hyperparameter tunning on 5 genes. Table S5. Prediction performance for the 1011 genes on validation and test sets. Table S6. Prediction performance for transcripts from established biomarker panels. Table S7. Correlation of gene expression with ER status (internal test set). Table S8. Correlation of gene expression with ER status (external test set). Table S9. Enriched pathways for genes cannot be predicted (Reactome gene sets). Table S10. Enriched pathways for genes cannot be predicted (Hallmark gene sets). Table S11. List of proliferation genes.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Tables from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Online-only supplementary tables: Table S1. Clinicopathological characteristics of patients per cohort. Table S2. ST gene panel. Table S3. The list of features used in the neural network based classification. Table S4. Results of pre-study hyperparameter tunning on 5 genes. Table S5. Prediction performance for the 1011 genes on validation and test sets. Table S6. Prediction performance for transcripts from established biomarker panels. Table S7. Correlation of gene expression with ER status (internal test set). Table S8. Correlation of gene expression with ER status (external test set). Table S9. Enriched pathways for genes cannot be predicted (Reactome gene sets). Table S10. Enriched pathways for genes cannot be predicted (Hallmark gene sets). Table S11. List of proliferation genes.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure S5 (PART1) from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
ST analysis results for Tumour(PART1)
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S5 (PART1) from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
ST analysis results for Tumour(PART1)
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure SF3 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Plot of delta area showing the relative change in area under the cumulative distribution function (CDF) curve from k-1 to k.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure SF3 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Plot of delta area showing the relative change in area under the cumulative distribution function (CDF) curve from k-1 to k.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure SF1 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Supplementary comparison of model performance
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure SF1 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Supplementary comparison of model performance
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure SF2 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Heat maps showing clusters of patients based on 44 genes (FDR adjusted p-value < 0.05) from the PAM50 gene panel.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure SF2 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Heat maps showing clusters of patients based on 44 genes (FDR adjusted p-value < 0.05) from the PAM50 gene panel.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure S6 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Correspondence between slide level IHC score and slide level EMO predictions.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S6 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Correspondence between slide level IHC score and slide level EMO predictions.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure SF3 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Plot of delta area showing the relative change in area under the cumulative distribution function (CDF) curve from k-1 to k.
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure SF3 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Plot of delta area showing the relative change in area under the cumulative distribution function (CDF) curve from k-1 to k.
- Open Access
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Figure SF1 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Supplementary comparison of model performance
- 30 March 2023
- Published by American Association for Cancer Research (AACR)
Open AccessFigure SF1 from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer
Supplementary comparison of model performance