Network analysis of driver genes in human cancers.

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences.

Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation.

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process.

A nearest-neighbors network model for sequence data reveals new insight into genotype distribution of a pathogen.

Background: Sequence similarity networks are useful for classifying and characterizing biologically important proteins. Threshold-based approaches to similarity network construction using exact distance measures are prohibitively slow to compute and rely on the difficult task of selecting an appropriate threshold, while similarity networks based on approximate distance calculations compromise useful structural information.

Results: We present an alternative network representation for a set of sequence data that overcomes these drawbacks.

Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer.

Human activity recognition (HAR) is an important component in health-care systems. For example, it can enable context-aware applications such as elderly care and patient monitoring. Relying on a set of training data, supervised machine learning algorithms form the core intelligence of most existing HAR systems.

Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach.

Recent advancements in mobile devices, data analysis, and wearable sensors render the capability of in-place health monitoring. Supervised machine learning algorithms, the core intelligence of these systems, learn from labeled training data. However, labeling vast amount of data is time-consuming and expensive.

Characterization of Anaplasma marginale subsp. centrale Strains by Use of msp1aS Genotyping Reveals a Wildlife Reservoir.

Bovine anaplasmosis caused by the intraerythrocytic rickettsial pathogen Anaplasma marginale is endemic in South Africa. Anaplasma marginale subspecies centrale also infects cattle; however, it causes a milder form of anaplasmosis and is used as a live vaccine against A. marginale There has been less interest in the epidemiology of A.

RepeatAnalyzer: a tool for analysing and managing short-sequence repeat data.

Background: Short-sequence repeats (SSRs) occur in both prokaryotic and eukaryotic DNA, inter- and intragenically, and may be exact or inexact copies. When heterogeneous SSRs are present in a given locus, we can take advantage of the pattern of different repeats to genotype strains based on the SSRs. Cataloguing and tracking these repeats can be difficult as diverse groups of researchers are involved in the identification of the repeats.