A privacy-preserving approach for cloud-based protein fold recognition.

The complexity and cost of training machine learning models have made cloud-based machine learning as a service (MLaaS) attractive for businesses and researchers. MLaaS eliminates the need for in-house expertise by providing pre-built models and infrastructure. However, it raises data privacy and model security concerns, especially in medical fields like protein fold recognition.

P/Key: PUF based second factor authentication.

One-time password (OTP) mechanisms are widely used to strengthen authentication processes. In time-based one-time password (TOTP) mechanisms, the client and server store common secrets. However, once the server is compromised, the client's secrets are easy to obtain.

Efficient privacy-preserving whole-genome variant queries.

Motivation: Diagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease-gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives.

Identifying disease-causing mutations with privacy protection.

Motivation: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data.

VCF-Explorer: filtering and analysing whole genome VCF files.

Summary: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies.

A fully automated microfluidic-based electrochemical sensor for real-time bacteria detection.

A fully automated microfluidic-based electrochemical biosensor was designed and manufactured for pathogen detection. The quantification of Escherichia coli was investigated with standard and nanomaterial amplified immunoassays in the concentration ranges of 0.99 × 103.

Lab-on-a-chip based biosensor for the real-time detection of aflatoxin.

Polymers were synthesized and utilized for aflatoxin detection coupled with a novel lab-on-a-chip biosensor: MiSens and high performance liquid chromatography (HPLC). Non-imprinted polymers (NIPs) were preferred to be designed and used due to the toxic nature of aflatoxin template and also to avoid difficult clean-up protocols. Towards an innovative miniaturized automated system, a novel biochip has been designed that consists of 6 working electrodes (1mm diameter) with shared reference and counter electrodes.

An integrated lab-on-a-chip-based electrochemical biosensor for rapid and sensitive detection of cancer biomarkers.

Recent advances in the area of biosensor technology and microfluidic applications have enabled the miniaturisation of the sensing platforms. Here we describe a new integrated and fully automated lab-on-a-chip-based biosensor device prototype (MiSens) that has potential to be used for point-of-care cancer biomarker testing. The key features of the device include a new biochip, a device integrated microfluidic system and real-time amperometric measurements during the flow of enzyme substrate.

FMFilter: A fast model based variant filtering tool.

The availability of whole exome and genome sequencing has completely changed the structure of genetic disease studies. It is now possible to solve the disease causing mechanisms within shorter time and budgets. For this reason, mining out the valuable information from the huge amount of data produced by next generation techniques becomes a challenging task.

Privacy preserving processing of genomic data: A survey.

Recently, the rapid advance in genome sequencing technology has led to production of huge amount of sensitive genomic data. However, a serious privacy challenge is confronted with increasing number of genetic tests as genomic data is the ultimate source of identity for humans. Lately, privacy threats and possible solutions regarding the undesired access to genomic data are discussed, however it is challenging to apply proposed solutions to real life problems due to the complex nature of security definitions.

Comparative transcriptome profiling approach to glean virulence and immunomodulation-related genes of Fasciola hepatica.

Background: Fasciola hepatica causes chronic liver disease, fasciolosis, leading to significant losses in the livestock economy and concerns for human health in many countries. The identification of F. hepatica genes involved in the parasite's virulence through modulation of host immune system is utmost important to comprehend evasion mechanisms of the parasite and develop more effective strategies against fasciolosis.

Generating a detailed protein profile of Fasciola hepatica during the chronic stage of infection in cattle.

Fasciola hepatica is a trematode helminth causing a damaging disease, fasciolosis, in ruminants and humans. Comprehensive proteomic studies broaden our knowledge of the parasite's protein profile, and provide new insights into the development of more effective strategies to deal with fasciolosis. The objective of this study was to generate a comprehensive profile of F.