Satdarshan P. Monga
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View article: Author Correction: Matrix viscoelasticity promotes liver cancer progression in the pre-cirrhotic liver
Author Correction: Matrix viscoelasticity promotes liver cancer progression in the pre-cirrhotic liver Open
View article: Multiomics identifies tumor-intrinsic SREBP1 driving immune exclusion in hepatocellular carcinoma
Multiomics identifies tumor-intrinsic SREBP1 driving immune exclusion in hepatocellular carcinoma Open
Immune checkpoint inhibitors (ICI) have improved patient outcomes in hepatocellular carcinoma (HCC); however, most patients do not experience durable benefit. The non-T cell-inflamed tumor microenvironment, characterized by limited CD8 + T…
View article: Precision targeting of β-catenin induces tumor reprogramming and immunity in hepatocellular cancers
Precision targeting of β-catenin induces tumor reprogramming and immunity in hepatocellular cancers Open
First-line immune checkpoint inhibitor (ICI) combinations show responses in subsets of hepatocellular carcinoma (HCC) patients. Nearly half of HCCs are Wnt-active with mutations in CTNNB1 (encoding for β-catenin), AXIN1/2 , or APC , and de…
View article: Table S8 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S8 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S8
View article: Figure S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S3
View article: Table S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S6
View article: Table S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S1
View article: Figure S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S2
View article: Table S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S4
View article: Table S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S7
View article: Figure S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S7
View article: Figure 4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure 4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Drug–target network in liver cancer. A, Drug–target network in liver cancer. The network was constructed by extracting drug–gene pairs associated with liver cancer from Fig. 3A, in which liver cancer was identified as a primary canc…
View article: Figure S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S2
View article: Figure 1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure 1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Automated LLM-based pipeline for drug–gene relationship inference. A, Pipeline design. The pipeline is designed to infer the relationship between a gene and a drug within a specific cancer type based on relevant literature. The proc…
View article: Figure S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S1
View article: Supplementary Methods from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Supplementary Methods from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Methods
View article: Table S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S2
View article: Data from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Data from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Understanding drug–gene relationships is essential for advancing targeted cancer therapies and drug repurposing strategies. However, the vast volume of biomedical literature poses significant challenges in efficiently extracting relevant i…
View article: Figure S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S3
View article: Table S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S6
View article: Table S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S3
View article: Figure S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S4
View article: Figure S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S6 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S6
View article: Table S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S2
View article: Table S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S1 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S1
View article: Supplementary Methods from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Supplementary Methods from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Methods
View article: Figure 2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure 2 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Example pipeline outputs and performance evaluation across retrieval approaches and LLMs. A, Pipeline output for an established drug–gene relationship between palbociclib and CDK4 in breast cancer. B, Output for CX-546…
View article: Table S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Table S7 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Table S7
View article: Figure S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure S4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Supplementary Figure S4
View article: Figure 3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Figure 3 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models Open
Pan-cancer drug–target interaction network. A, The network includes 854 drug–gene pairs identified by our pipeline through pairwise combinations of 214 oncogenes and 144 FDA-approved oncology drugs as high-confidence drug–gene inter…