Liewei Wang
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View article: Quantifying Sample Representation in Global Pharmacogenomic Studies of Major Depressive Disorder: A Systematic Review
Quantifying Sample Representation in Global Pharmacogenomic Studies of Major Depressive Disorder: A Systematic Review Open
Major depressive disorder (MDD) is a substantial public health challenge. Pharmacogenomics (PGx), which identifies genetic variations that predict drug treatment outcomes, may have utility for clinical practice, but adequate representation…
View article: Targeting MTAP increases PARP inhibitor susceptibility in triple-negative breast cancer through a feed-forward loop
Targeting MTAP increases PARP inhibitor susceptibility in triple-negative breast cancer through a feed-forward loop Open
Triple-negative breast cancer (TNBC) represents the most malignant subtype of breast cancer. The clinical application of PARP inhibitors (PARPi) is limited by the low frequency of BRCA1/2 mutations in TNBC. Here, we identified that MTAP de…
View article: Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder
Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder Open
Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. …
View article: Advancing efficacy prediction for electronic health records based emulated trials in repurposing heart failure therapies
Advancing efficacy prediction for electronic health records based emulated trials in repurposing heart failure therapies Open
The complexities inherent in EHR data create discrepancies between real-world evidence and RCTs, posing substantial challenges in determining whether a treatment is likely to have a beneficial impact compared to the standard of care in RCT…
View article: SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction
SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction Open
The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Larg…
View article: Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer
Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer Open
The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and…
View article: OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks
OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks Open
The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing dee…
View article: Figure S4 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S4 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S4. Biodistribution Study
View article: Figure S2 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S2 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S2. Capture Efficiency of Trop-2 Expressing Cell Lines.
View article: Data from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Data from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying mole…
View article: Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary File 3: Excel file with Supplementary tables S6 - S9
View article: Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary Notes (S1-S5) Supplementary Tables (S1 - S5; S10 - S11) Supplementary Figures (S1 - S12)
View article: Data from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Data from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
PURPOSE. Men with metastatic castration-resistant prostate cancer (mCRPC) frequently develop resistance to androgen receptor signaling inhibitor (ARSI) treatment; therefore, new therapies are needed. Trophoblastic cell-surface antigen (Tro…
View article: Figure S2 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S2 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S2. Capture Efficiency of Trop-2 Expressing Cell Lines.
View article: Figure S1 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S1 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S1. Correlation of TACSTD2 with Prostate Cancer Cell Surface Markers.
View article: Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Excel file with regulon edges and ChIPseq enrichment scores for TraRe, GRNboost2 and ARACNE-AP
View article: Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 3 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary File 3: Excel file with Supplementary tables S6 - S9
View article: Figure S3 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S3 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S3. Scatter plot of gene expression for genes shown in Figure 3D isolated from matched anti-EpCAM vs anti-Trop2 captured CTCs at the same timepoint.
View article: Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary File 2: Excel file with CommunityAMARETTO Enrichment results
View article: Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 4 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Excel file with regulon edges and ChIPseq enrichment scores for TraRe, GRNboost2 and ARACNE-AP
View article: Figure S4 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S4 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S4. Biodistribution Study
View article: Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 1 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary Notes (S1-S5) Supplementary Tables (S1 - S5; S10 - S11) Supplementary Figures (S1 - S12)
View article: Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer
Supplementary File 2 from Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer Open
Supplementary File 2: Excel file with CommunityAMARETTO Enrichment results
View article: Figure S1 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S1 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S1. Correlation of TACSTD2 with Prostate Cancer Cell Surface Markers.
View article: Figure S3 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer
Figure S3 from Expression and therapeutic targeting of Trop-2 in treatment resistant prostate cancer Open
Figure S3. Scatter plot of gene expression for genes shown in Figure 3D isolated from matched anti-EpCAM vs anti-Trop2 captured CTCs at the same timepoint.
View article: Data from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial
Data from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial Open
Purpose:We previously reported that postmenopausal women with estrogen receptor-α–positive breast cancer receiving adjuvant anastrozole 1 mg/day (ANA1) with estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL [inadequate estrogen suppres…
View article: Supplemental Table 2 from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial
Supplemental Table 2 from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial Open
Supplemental Table 2: All adverse events among those patients who discontinued ANA1 early or did not continue onto ANA10 after IES finding due to toxicity.
View article: Data from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial
Data from Anastrozole Dose Escalation for Optimal Estrogen Suppression in Postmenopausal Early-Stage Breast Cancer: A Prospective Trial Open
Purpose:We previously reported that postmenopausal women with estrogen receptor-α–positive breast cancer receiving adjuvant anastrozole 1 mg/day (ANA1) with estrone (E1) ≥1.3 pg/mL and estradiol (E2) ≥0.5 pg/mL [inadequate estrogen suppres…