Featured Publications

Fig 1 from "CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer"
Fig 6 from "Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data"
Figure 4 from "The Importance of Neighborhood Scheme Selection in Agent-based Tumor Growth Modeling"



Editorial: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes

Computer simulation of cancer data and processes in silico is vital to making progress in cancer research. While there have been many advances in systems biology, statistical methods, data science and machine learning on both basic and clinical biomedical research levels, mathematical modeling and computer simulation of cancer still play an important role in developing computer-aided diagnosis and in the optimization of clinical tools. This supplement solicited papers on all aspects of computer simulation, visualization and image processing of cancer data and processes… more >

Fig 1 from "The role of markup for enabling interoperability in health informatics"
Fig 3 from "The role of markup for enabling interoperability in health informatics"



The Role of Markup for Enabling Interoperability in Health Informatics

In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things people’s expectations are for greater interconnectivity and seamless interoperability. We begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable… more >

Fig 1 from Optimising parallel R correlation matrix calculations on gene expression data using MapReduce
Fig 3 from "Optimising parallel R correlation matrix calculations on gene expression data using MapReduce"
Fig 6 from "Optimising parallel R correlation matrix calculations on gene expression data using MapReduce"



Optimising Parallel R Correlation Matrix Calculations on Gene Expression Data Using MapReduce

High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation.In this paper, we evaluate the current parallel modes for correlation calculation methods and introduce an efficient data distribution and parallel calculation algorithm based on MapReduce to optimise the correlation calculation. We studied the performance of our algorithm using two gene expression benchmarks… more >

You can find a more complete list of publications on my Google Scholar profile >