Jacob G. Scott

This study develops an in vitro model of osteosarcoma chemoresistance and identifies predictive gene expression signatures for collateral drug responses to guide second-line treatment.

This study uses a discrete-time tumor-immune model to analyze stability transitions and bifurcations under immune suppression, showing how parameter changes affect tumor control and immune escape.

This paper develops an RNA sequencing-based radiosensitivity index (RSI-seq) that preserves the clinical utility of the genomic-adjusted radiation dose (GARD) model, enabling personalized radiotherapy across diverse cancer types.

This study shows that stromal niches protect lung cancers from ALK inhibitors through multiple mechanisms, and targeting these mechanisms together is more effective than targeting them individually.

The paper shows that targeted therapy resistance in cancer selects for cells with higher phenotypic plasticity, which enhances their ability to adapt to other stressors and promotes metastasis.

This paper introduces SHEPHERD, a method combining population genetics and MDPs to design adaptive drug policies that reduce disease fitness in heterogeneous populations, showing effectiveness in synthetic multi-drug landscapes.

The paper experimentally evolves resistance in Ewings sarcoma cell lines to identify gene expression signatures predicting collateral drug responses, validated in an independent cell line.

This paper introduces a framework called the chemogram to rank drug sensitivity using transcriptomic signatures, showing improved accuracy over random predictions for multiple chemotherapy drugs.

The paper develops a multiplexed mouse model to study TBX2 subfamily and Egr1 roles in Ras-driven lung cancer, finding Egr1 loss significantly increases tumor size and suggests immune dysregulation.

This paper uses spatial statistics from single time-point images to classify game-theoretic interactions between drug-sensitive and resistant cancer cells using an agent-based model.

This paper introduces a new metric, edge flip fraction, to compare fitness landscapes and shows it predicts evolutionary trajectories and drug sequence optimality.

The paper studies how asymmetric interactions affect mutant survival during population range expansions using simulations and a mathematical model.

This study shows that cell-cell fusion in NSCLC leads to increased chemoresistance and tumor aggressiveness by creating hybrid cells with enhanced fitness and genomic heterogeneity.

This paper introduces a fluorescence-based time-kill assay using resazurin to measure dose-dependent cell death in bacteria, showing improved accuracy and reduced resource use compared to existing methods.

Crosstalkr identifies drug targets and disease subnetworks using protein-protein interaction networks and random walk methods.

The paper introduces graph texture features, a novel method for analyzing network structures, and applies it to cancer informatics to classify cell lines by lineage with high accuracy.

This paper identifies therapeutic miRNA cocktails for Ewing Sarcoma using a network science-based approach to prioritize miRNA targets and combinations.

The paper evaluates how variability in scRNA-Seq analysis can be reduced using supervised frameworks or regularization, showing improved consistency across different methods.

The paper shows how non-coding RNA acting as miRNA sponges can generate oscillatory dynamics in a transcriptional feedback network, with implications for understanding RNA species roles in biological processes.

egtplot is a Python package for visualizing 3-strategy evolutionary games, providing functions for parameter sweeps and animations.

The paper investigates miRNA regulation across 15 cancer types, finding complex interactions between miRNA expression and cancer hallmarks, with specific emphasis on the oncomiR-1 cluster and repression of tumour suppressor genes.

This paper introduces sigQC, an R package that standardizes gene signature quality control, demonstrating its application in evaluating signatures on cancer gene expression data.

This study shows that resistance to ALK inhibitors in lung cancer is unstable and dynamic, with collateral sensitivity to other drugs offering new treatment options.

The paper introduces MultiCellDS, a standard and community-driven initiative for sharing multicellular data to enhance research in cell biology.

The paper introduces MultiCellDS, a standard for multicellular data curation, and presents MultiCellDB as a database to store associated data, enhancing data sharing and utility in biological and clinical research.

This paper explores using a lymphocyte meta-phenotype to improve adoptive cell therapy outcomes by integrating patient data with mathematical models.