Anil Korkut

This paper evaluates cross-slice protein imputation methods, finding that learning-based approaches outperform alignment-based ones for imputation, while alignment-based methods are better for interpolation and 3D reconstruction.

CROCHET is a pipeline for automated analysis of single-cell spatialomic data and creation of visual atlases, supporting diverse data modalities and downstream analyses.

The paper identifies three intrinsic metastatic breast cancer subtypes driven by Trp53 mutations through multi-omics analysis, revealing distinct transcriptomic and genomic features.

The paper uses protein mass spectrometry to analyze drug responses in ovarian cancer cells, identifies resistance mechanisms, and proposes drug combinations and single agents to overcome resistance.

This study creates a spatial proteomic atlas of SBA tumor-immune microenvironment, revealing cell interactions and immune checkpoint patterns linked to patient survival and therapy.

RECOMBINE identifies recurrent composite markers for cell types and states, outperforming existing methods in accuracy and revealing novel markers across tissues and conditions.

The paper introduces ImogiMap, a statistical framework to identify tumor-immune interactions by analyzing their co-association with immune phenotypes, and identifies the CD86/CD70 axis as a potential immunotherapy target in endometrial carcinoma.

The paper introduces a computational method to generate causal explanations for proteomic profiles by integrating molecular data with prior biological pathway knowledge, demonstrating its application in various biological contexts.

This paper introduces TargetScore, a method to identify adaptive responses and predict drug-induced vulnerabilities in cancer cells, validated through experiments on breast cancer cells.

The paper introduces a hybrid machine learning approach for perturbation biology to identify combinatorial therapeutic candidates using a melanoma cell line dataset.

The paper compiles protein response data from cancer cell lines to drug perturbations, showing improved drug sensitivity prediction and building a protein-drug connectivity map with an accessible data portal.

The paper develops a temporal modeling framework to link protein changes to drug responses in melanoma cells, identifying effective therapies through experimental validation.

The paper evaluates the Zeptosens RPPA platform for protein profiling in cancer samples, demonstrating its sensitivity, reproducibility, and accuracy.

The paper identifies rare functional mutations in cancer by mapping them to protein domain hotspots across 22 tumor types, revealing both known and novel mutations with functional implications.

The paper uses perturbation biology to predict c-Myc as a co-target with BRAF/MEK inhibitors to overcome RAF inhibitor resistance in melanoma.