![Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies | npj Precision Oncology Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies | npj Precision Oncology](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41698-021-00232-w/MediaObjects/41698_2021_232_Fig1_HTML.png)
Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies | npj Precision Oncology
![Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force | SpringerLink Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force | SpringerLink](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs00330-022-08880-7/MediaObjects/330_2022_8880_Fig1_HTML.png)
Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force | SpringerLink
![Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends](https://www.frontiersin.org/files/Articles/1086097/fmed-10-1086097-HTML/image_m/fmed-10-1086097-g001.jpg)
Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules | PLOS Computational Biology
In Silico and In Cell Hybrid Selection of Nonrapalog Ligands to Allosterically Inhibit the Kinase Activity of mTORC1 | Journal of Medicinal Chemistry
![Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends](https://www.frontiersin.org/files/Articles/1086097/fmed-10-1086097-HTML/image_m/fmed-10-1086097-g002.jpg)
Frontiers | Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
![In Silico and In Cell Hybrid Selection of Nonrapalog Ligands to Allosterically Inhibit the Kinase Activity of mTORC1 | Journal of Medicinal Chemistry In Silico and In Cell Hybrid Selection of Nonrapalog Ligands to Allosterically Inhibit the Kinase Activity of mTORC1 | Journal of Medicinal Chemistry](https://pubs.acs.org/cms/10.1021/acs.jmedchem.1c00536/asset/images/large/jm1c00536_0006.jpeg)
In Silico and In Cell Hybrid Selection of Nonrapalog Ligands to Allosterically Inhibit the Kinase Activity of mTORC1 | Journal of Medicinal Chemistry
![Cancers | Free Full-Text | Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma Cancers | Free Full-Text | Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma](https://pub.mdpi-res.com/cancers/cancers-12-03493/article_deploy/html/images/cancers-12-03493-ag.png?1607096110)
Cancers | Free Full-Text | Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma
![Cancers | Free Full-Text | Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma Cancers | Free Full-Text | Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma](https://www.mdpi.com/cancers/cancers-12-03493/article_deploy/html/images/cancers-12-03493-g001.png)