The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as ...

Mass spectrometry (MS)-based metabolomics analysis is frequently used due to broad analyte coverage, high sensitivity, high selectivity, and high performance 1. The MS-based metabolomic analytical ...

A research team at POSTECH led by Professor Wook-Shin Han of the Department of Computer Science and Engineering and the ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management.

Graph technology has become a requirement for the modern enterprise. Companies in virtually every industry, from healthcare to energy to financial services, are applying the power of graph analytics ...

Graph databases are gaining attention as enterprises work on their next-generation artificial intelligence (AI) applications. While still a bit of an outlier, graph-oriented databases continue to find ...

Graphs, maps and data analyses? Now ChatGPT can do even more. By Yiwen Lu Reporting from San Francisco ChatGPT, the artificial-intelligence-powered chatbot made by OpenAI, has wowed the world in ...

In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...