Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Interferometric detection and enumeration of viral particles using Si-based microfluidics

Single-particle interferometric reflectance imaging sensor enables optical visualization and characterization of individual nanoparticles without any labels. Using this technique, we have shown end-point and real-time detection of viral particles using laminate-based active and passive cartridge configurations. Here, we present a new concept for low-cost microfluidic integration of the sensor chips into compact cartridges through utilization of readily available silicon fabrication technologies. This new cartridge configuration will allow simultaneous detection of individual virus binding events on a 9-spot microarray, and provide the needed simplicity and robustness for routine real-time operation for discrete detection of viral particles in a multiplex format.This work was supported in part by a research contract with the ASELSAN Research Center, Ankara, Turkey, and in part by the European Union's Horizon 2020 FET Open program under Grant 766466-INDEX. (ASELSAN Research Center, Ankara, Turkey; 766466-INDEX - European Union's Horizon 2020 FET Open program)First author draf