Skip to content

Author

M. K. Niazi

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

Patient-specific integration of RNA expression, methylation-derived replication-timing proxies, and histomorphology for pancreatic cancer subtyping

Network biology uses gene correlations to characterize biological pathways. Linear Interpolation to Obtain Network Estimates for Single Samples (LIONESS) enables individualized gene networks, but the influence of methylation derived replication timing and tissue morphology on these networks remains unclear. We integrat...

Alejandro Leyva, M. Niazi · 0 citations
Open access Oct 2026

Single-Cell Multiplex Immune Biomarker Prediction from Histopathology Across Cancer Types and Clinical Endpoints

Background The spatial organization of cellular lineages within the tumor microenvironment governs disease progression and therapeutic response, but multiplexed spatial profiling remains costly, technically complex, and tissue-destructive. Routine hematoxylin and eosin (H&E) histopathology is inexpensive and widely ava...

Usman Afzaal, Usama Sajjad, A. Akbar et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images

Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and the tissue morphologies carrying prognostic information remain incompletely characterized. Methods: We conducted a retrospective secondary analysis using TCGA...

Hikmat Khan, M. Gurcan, Anil Parwani et al. · 0 citations
#protein folding Open access Oct 2026

Gene-structured histology for deriving and predicting pancreatic cancer molecular subtypes

into basal-like and classical states is a critical prognostic determinant, yet clinical implementation remains limited by the cost and turnaround time of transcriptomic sequencing. 1 – 3 Although routine histopathology captures rich morphological features, deep learning models often lack a principled connection to gene...

Alejandro Leyva, Abdul Rehman Akbar, M. Niazi · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.