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Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|
May 3, 2017
Why we use more materials
Timothy Gutowski, Daniel Cooper, Sahil Sahni
Nature Machine Intelligence
|
September 29, 2025
Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data
Yingying Cao, Tian-Gen Chang, Sahil Sahni, et al.
Environmental Science & Technology
|
April 26, 2011
Remanufacturing and energy savings
Timothy G Gutowski, Sahil Sahni, Avid Boustani, et al.
Iscience
|
June 4, 2024
Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction
Binbin Wang, Kun Wang, Di Wu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|
January 30, 2013
The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand
Timothy G Gutowski, Sahil Sahni, Julian M Allwood, et al.
Biorxiv : the Preprint Server for Biology
|
October 27, 2023
Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade
Sahil Sahni, Binbin Wang, Di Wu, et al.
Nature Communications
|
October 14, 2024
A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade
Sahil Sahni, Binbin Wang, Di Wu, et al.
Nature Communications
|
October 2, 2024
ZIC2 and ZIC3 promote SWI/SNF recruitment to safeguard progression towards human primed pluripotency
Ishtiaque Hossain, Pierre Priam, Sofia C Reynoso, et al.
Cancer Letters
|
November 1, 2025
Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells
Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, et al.
Biorxiv : the Preprint Server for Biology
|
October 7, 2024
The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy
Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, et al.
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of 2
Search research articles
Search
Showing results (1-10 of 12) with videos related to
Sort By:
Page
of 2
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|
May 3, 2017
Why we use more materials
Timothy Gutowski, Daniel Cooper, Sahil Sahni
Nature Machine Intelligence
|
September 29, 2025
Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data
Yingying Cao, Tian-Gen Chang, Sahil Sahni, et al.
Environmental Science & Technology
|
April 26, 2011
Remanufacturing and energy savings
Timothy G Gutowski, Sahil Sahni, Avid Boustani, et al.
Iscience
|
June 4, 2024
Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction
Binbin Wang, Kun Wang, Di Wu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|
January 30, 2013
The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand
Timothy G Gutowski, Sahil Sahni, Julian M Allwood, et al.
Biorxiv : the Preprint Server for Biology
|
October 27, 2023
Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade
Sahil Sahni, Binbin Wang, Di Wu, et al.
Nature Communications
|
October 14, 2024
A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade
Sahil Sahni, Binbin Wang, Di Wu, et al.
Nature Communications
|
October 2, 2024
ZIC2 and ZIC3 promote SWI/SNF recruitment to safeguard progression towards human primed pluripotency
Ishtiaque Hossain, Pierre Priam, Sofia C Reynoso, et al.
Cancer Letters
|
November 1, 2025
Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells
Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, et al.
Biorxiv : the Preprint Server for Biology
|
October 7, 2024
The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy
Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, et al.
Page
of 2