日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells

通过整合免疫细胞、基质细胞和肿瘤细胞的解卷积表达模式,提高对乳腺癌患者化疗反应的预测能力。

Dhruba, Saugato Rahman; Sahni, Sahil; Wang, Binbin; Wu, Di; Rajagopal, Padma Sheila; Schmidt, Yael; Shulman, Eldad D; Sinha, Sanju; Sammut, Stephen-John; Caldas, Carlos; Wang, Kun; Ruppin, Eytan

Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology

Path2Omics 提高了基于肿瘤组织病理学的转录组和甲基化预测准确性

Hoang, Danh-Tai; Shulman, Eldad D; Dhruba, Saugato Rahman; Nair, Nishanth Ulhas; Barman, Ranjan Kumar; Cantore, Thomas; Biswas, Sumona; Lalchungnunga, H; Singh, Omkar; Chung, Youngmin; Lee, Joo Sang; Nasrallah, MacLean P; Stone, Eric A; Aldape, Kenneth; Ruppin, Eytan

Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma

纵向液体活检可识别头颈部鳞状细胞癌免疫检查点阻断反应的早期预测性生物标志物

Wang, Binbin; Saddawi-Konefka, Robert; Clubb, Lauren M; Tang, Shiqi; Wu, Di; Mukherjee, Sumit; Sahni, Sahil; Dhruba, Saugato Rahman; Yang, Xinping; Patiyal, Sumeet; Day, Chi-Ping; Desai, Parth A; Allen, Clint; Wang, Kun; Gutkind, J Silvio; Ruppin, Eytan

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

基于人工智能推断的核特征,对乳腺癌进行病理学家可解读的亚型分类和分层。

Barman, Ranjan Kumar; Dhruba, Saugato Rahman; Hoang, Danh-Tai; Shulman, Eldad D; Campagnolo, Emma M; Wang, Andy T; Harmon, Stephanie A; Hu, Tom C; Papanicolau-Sengos, Antonios; Nasrallah, MacLean P; Aldape, Kenneth D; Ruppin, Eytan

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

基于肿瘤转录组学的机器学习框架在监督式治疗反应预测中的应用:一项大规模泛癌研究

Pal, Lipika Ray; Gertz, Edward Michael; Ulhas Nair, Nishanth; Mukherjee, Sumit; Patiyal, Sumeet; Cantore, Thomas; Campagnolo, Emma M; Chang, Tian-Gen; Dhruba, Saugato Rahman; Kim, Yewon; Shulman, Eldad David; Rajagopal, Padma Sheila; Hoang, Danh-Tai; Schäffer, Alejandro A; Ruppin, Eytan

Deep learning inference of cell type-specific gene expression from breast tumor histopathology

利用深度学习从乳腺肿瘤组织病理学中推断细胞类型特异性基因表达

Wang, Andrew T; Dhruba, Saugato R; Wang, Kun; Campagnolo, Emma M; Shulman, Eldad D; Ruppin, Eytan

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Path2Omics:基于肿瘤组织病理学的转录组和甲基化预测准确性增强

Hoang, Danh-Tai; Shulman, Eldad D; Dhruba, Saugato Rahman; Nair, Nishanth Ulhas; Barman, Ranjan K; Lalchungnunga, H; Singh, Omkar; Nasrallah, MacLean P; Stone, Eric A; Aldape, Kenneth; Ruppin, Eytan

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

基于肿瘤转录组学和组织病理学的泛癌肿瘤免疫激活和对免疫检查点阻断反应的预测

Mukherjee, Sumit; Patiyal, Sumeet; Pal, Lipika R; Chang, Tian-Gen; Biswas, Sumona; Dhruba, Saugato Rahman; Stemmer, Amos; Singh, Arashdeep; Yousefi-Rad, Abbas; Chen, Tien-Hua; Wang, Binbin; Marino, Denis; Shon, Wonwoo; Yuan, Yuan; Faries, Mark; Hamid, Omid; Reckamp, Karen; Waissengrin, Barliz; Ornelas, Beatriz; Chu, Pen-Yuan; Boudjadi, Salah; Ley, Lisa; Akbulut, Dilara; Ahmar, Nourhan El; Signoretti, Sabina; Braun, David A; Joo, Hyunjeong; Kim, Hyungsoo; Osipov, Arsen; Figlin, Robert A; Bar, Jair; Barshack, Iris; Day, Chi-Ping; Sargsyan, Karine; Apolo, Andrea B; Aldape, Kenneth; Yang, Muh-Hwa; Atkins, Michael B; Ronai, Ze'ev A; Hoang, Danh-Tai; Ruppin, Eytan

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

利用单细胞引导技术鉴定逻辑门控抗原组合,以设计有效且安全的CAR疗法

Madan, Sanna; Chang, Tian-Gen; Harris, Alexandra R; Liu, Huaitian; Martinez, Andrew; Dhruba, Saugato Rahman; Wang, Binbin; Rajagopal, Padma Sheila; Sinha, Sanju; Srinivasan, Aravind; Knott, Simon R V; Sayed, Shahin; Makokha, Francis; Day, Chi-Ping; Gierach, Gretchen L; Ambs, Stefan; Schäffer, Alejandro A; Ruppin, Eytan

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

LORIS利用常见的临床、病理和基因组特征,能够可靠地预测接受免疫检查点阻断疗法的患者预后。

Chang, Tian-Gen; Cao, Yingying; Sfreddo, Hannah J; Dhruba, Saugato Rahman; Lee, Se-Hoon; Valero, Cristina; Yoo, Seong-Keun; Chowell, Diego; Morris, Luc G T; Ruppin, Eytan