Krishna R. Kalari
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View article: An interpretable and adaptive autoencoder for efficient tissue deconvolution
An interpretable and adaptive autoencoder for efficient tissue deconvolution Open
Deconvolution models are a powerful tool for extracting cell-type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-seque…
View article: Novel set of plasma proteins classifies Alzheimer's dementia in African American individuals with high accuracy
Novel set of plasma proteins classifies Alzheimer's dementia in African American individuals with high accuracy Open
INTRODUCTION African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. …
View article: Abstract 2932 Network-Based Profiling of TNBC: Phosphoproteomics analysis of TNBC patient samples reveals targets for precision oncology
Abstract 2932 Network-Based Profiling of TNBC: Phosphoproteomics analysis of TNBC patient samples reveals targets for precision oncology Open
View article: Characterization of transcriptomic changes in the neurovascular unit of Alzheimiers transgenic mouse models using digital spatial profiling
Characterization of transcriptomic changes in the neurovascular unit of Alzheimiers transgenic mouse models using digital spatial profiling Open
Alzheimer’s disease (AD) affects 40 million individuals globally and is characterized by the accumulation of amyloid-beta (Aβ) proteins, which aggregate and form plaques. BBB dysfunction drives AD cerebrovascular pathology and BBB integrit…
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
View article: Validation of histopathology foundation models through whole slide image retrieval
Validation of histopathology foundation models through whole slide image retrieval Open
View article: Elucidating Molecular Mechanisms Governing TNF-Alpha-Mediated Regulation of Amyloid Beta 42 Uptake in Blood-Brain Barrier Endothelial Cells
Elucidating Molecular Mechanisms Governing TNF-Alpha-Mediated Regulation of Amyloid Beta 42 Uptake in Blood-Brain Barrier Endothelial Cells Open
Cerebrovascular inflammation is prevalent in a majority of Alzheimer’s patients. Inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha), circulating in the plasma have been shown to cause the inflammation of blood-brain ba…
View article: QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid‐Beta Exposure in Upregulating <scp>VCAM1</scp> Expression at the BBB Endothelium
QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid‐Beta Exposure in Upregulating <span>VCAM1</span> Expression at the BBB Endothelium Open
Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link betwee…
View article: Molecular Mechanisms Underlying Amyloid Beta Peptide Mediated Upregulation of Vascular Cell Adhesion Molecule-1 in Alzheimer Disease
Molecular Mechanisms Underlying Amyloid Beta Peptide Mediated Upregulation of Vascular Cell Adhesion Molecule-1 in Alzheimer Disease Open
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: 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: A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology
A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology Open
Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, …
View article: Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models
Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models Open
We have tested recently published foundation models for histopathology for image retrieval. We report macro average of F1 score for top-1 retrieval, majority of top-3 retrievals, and majority of top-5 retrievals. We perform zero-shot retri…
View article: Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer’s disease dementia with high accuracy in an African American cohort
Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer’s disease dementia with high accuracy in an African American cohort Open
Introduction African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer’s disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. Methods Untargeted proteome measur…
View article: Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer
Pre-treatment peripheral blood immunophenotyping and response to neoadjuvant chemotherapy in operable breast cancer Open
Background Tumor immune infiltration and peripheral blood immune signatures have prognostic and predictive value in breast cancer. Whether distinct peripheral blood immune phenotypes are associated with response to neoadjuvant chemotherapy…
View article: ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery
ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery Open
Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex…
View article: Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer
Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer Open
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: Identification of a Notch transcriptomic signature for breast cancer
Identification of a Notch transcriptomic signature for breast cancer Open
Background Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacki…
View article: Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer
Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer Open
View article: Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution Open
Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high cost renders it impractical for extensive sample cohorts w…
View article: Figure S2 from Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1–positive Triple-negative Breast Cancer
Figure S2 from Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1–positive Triple-negative Breast Cancer Open
Supplementary Figure S2. Kaplan Meier survival analysis evaluating association of PD-L1+ (SP142 assay with {greater than or equal to}1% IC+, in red) to PD-L1- tumors (SP142 assay <1% IC+ in black) with recurrence-free survival (RFS) (A) or…