Prakash Lingasamy

Integrative post-GWAS analysis prioritizes immune regulatory pathways and candidate effector signals in systemic lupus erythematosus

Vijayachitra Modhukur, Masuma Khatun, Naisarg Patel, Sajitha Lulu S et autres

Background Systemic lupus erythematosus (SLE) has a complex polygenic architecture, but translating genome-wide association signals into biologically interpretable candidates remains challenging. We applied an integrative post-GWAS framework to refine SLE-associated loci and prioritize candidate regulatory mechanisms. Methods European-ancestry SLE GWAS summary statistics …

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Natural compounds as epigenetic modulators in gynaecological cancers: From chemoresistance to precision oncology

Prakash Lingasamy, Blessy Kiruba, Santhosh Rajakumar, Suhas Manikant Surisetti et autres

BACKGROUND: Gynaecological cancers, including ovarian, cervical, and endometrial malignancies, remain major causes of cancer-related morbidity and mortality because of tumour heterogeneity, recurrence, and therapeutic resistance. Epigenetic dysregulation, involving aberrant DNA methylation, altered histone modifications, dysregulated non-coding RNAs, and N⁶-methyladenosine (m⁶A) RNA remodelling, …

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Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Abstract Background High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer–related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; …

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Additional file 1 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 1. Supplementary Method S1: TCGA-HGSOC cohort selection and slide inclusion criteria. Supplementary Method S2: Whole-slide image preprocessing, tissue masking, and tile quality filtering. Supplementary Method S3: Self-supervised contrastive learning (MoCo v2) training details. Supplementary Method S4: Slide-level embedding aggregation and …

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Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Abstract Background High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer–related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; …

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Additional file 2 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 2. Supplementary Table-1: Candidate genes, RefSeq identifiers, and primers sequences details for RT-qPCR validation of candidate genes in the independent HGSOC cohort. Supplementary Table-2: MoCo v2 Architecture, training hyperparameters, and augmentation settings used for self-supervised representation learning. Supplementary Table-3: Hyperparameters …

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Additional file 3 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 3. Supplementary Figure S1: Number of protein–ligand hydrogen bonds formed over the course of the simulations. (A) 4-Heptyloxyphenol (Control) (B) Cubebin (C) Hinokinin (D) Matairesinol. Supplementary Figure S2: Time evolution of solvation free energy (left) and solvent-accessible surface area (right) …

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Additional file 3 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 3. Supplementary Figure S1: Number of protein–ligand hydrogen bonds formed over the course of the simulations. (A) 4-Heptyloxyphenol (Control) (B) Cubebin (C) Hinokinin (D) Matairesinol. Supplementary Figure S2: Time evolution of solvation free energy (left) and solvent-accessible surface area (right) …

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Additional file 1 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 1. Supplementary Method S1: TCGA-HGSOC cohort selection and slide inclusion criteria. Supplementary Method S2: Whole-slide image preprocessing, tissue masking, and tile quality filtering. Supplementary Method S3: Self-supervised contrastive learning (MoCo v2) training details. Supplementary Method S4: Slide-level embedding aggregation and …

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Additional file 2 of Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Supplementary Material 2. Supplementary Table-1: Candidate genes, RefSeq identifiers, and primers sequences details for RT-qPCR validation of candidate genes in the independent HGSOC cohort. Supplementary Table-2: MoCo v2 Architecture, training hyperparameters, and augmentation settings used for self-supervised representation learning. Supplementary Table-3: Hyperparameters …

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Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Prakash Lingasamy, Marta Ostrowska-Leśko, Pantelis Tsakalis, Naisarg Patel et autres

Abstract Background High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer–related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; …

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Integrative Post-GWAS Analysis Prioritizes Immune Regulatory Pathways and Candidate Effector Signals in Systemic Lupus Erythematosus

Vijayachitra Modhukur, Masuma Khatun, Naisarg Patel, Sajitha Lulu S et autres

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