Charles Blatti
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View article: Nasal and systemic immune responses correlate with viral shedding after influenza challenge in people with complex preexisting immunity
Nasal and systemic immune responses correlate with viral shedding after influenza challenge in people with complex preexisting immunity Open
Each year in the United States, ~50% of adults ≥18 years old are vaccinated against influenza viruses, with protective efficacy averaging 40.5% over the past 20 years. To model annual seasonal influenza, a cohort of 74 adults, who were uns…
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: 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: 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: 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: 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: 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 ChIP-seq enrichment scores for TraRe, GRNboost2 and ARACNE-AP
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 ChIP-seq enrichment scores for TraRe, GRNboost2 and ARACNE-AP
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 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 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: 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 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 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: Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
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: Identification of transcriptional network disruptions in drug-resistant prostate cancer with TraRe
Identification of transcriptional network disruptions in drug-resistant prostate cancer with TraRe Open
Metastatic castration-resistant prostate cancer (mCRPC) presents very low survival rates due to lack of response or acquired resistance to the available therapies. To date no molecular mechanisms of resistance have been identified, pointin…
View article: Lung epithelial and endothelial damage, loss of tissue repair, inhibition of fibrinolysis, and cellular senescence in fatal COVID-19
Lung epithelial and endothelial damage, loss of tissue repair, inhibition of fibrinolysis, and cellular senescence in fatal COVID-19 Open
Lung epithelial and endothelial cell damage and defective lung tissue repair contribute to fatal COVID-19.
View article: Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks Open
Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characteriz…
View article: Author response: Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks
Author response: Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks Open
View article: Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks
Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks Open
Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characteriz…
View article: Uncovering Effective Explanations for Interactive Genomic Data Analysis
Uncovering Effective Explanations for Interactive Genomic Data Analysis Open
View article: GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis
GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis Open
Motivation A common but critical task in genomic data analysis is finding features that separate and thereby help explain differences between two classes of biological objects, e.g., genes that explain the differences between healthy and d…
View article: Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform
Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform Open
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene priorit…
View article: Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform
Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform Open
We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene…