Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Moamin Abughazala
I hereby declare that the thesis entitled "Architecting Data-Intensive Applications: From Data Architecture Design to Its Quality Assurance," which I am submitting as part of my doctoral program at the University of L'Aquila, is a bona fide representation of my work. This research was undertaken under the supervision…
Shantenu Jha, Daniel S. Katz, André Luckow, Omer Rana + 2 more
'Yogesh Simmhan' 'Neil Chue Hong'] A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often…
Ivan Merelli, Horacio Pérez-Sánchez, Sandra Gesing, Daniele D'Agostino
"Daniele D'Agostino"] The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with…
Bingbing Rao, Liqang Wang
—We are living in the era of Big Data and witnessing the explosion of data. Given that the limitation of CPU and I/O in a single computer, the mainstream approach to scalability is to distribute computations among a large number of processing nodes in a cluster or cloud. This paradigm gives rise to the term of…
Taylor Reiter, Phillip T. Brooks, Luiz Irber, Shannon E.K. Joslin + 4 more
As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter…
Tobias Ueli Blatter, Harald Witte, Christos Theodoros Nakas, Alexander Benedikt Leichtle + 1 more
'Alexander Benedikt Leichtle' 'Zhongheng Zhang'] Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day-from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing…
Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat + 4 more
'Zakira Inayat' 'Waleed Kamaleldin Mahmoud Ali' 'Muhammad Alam' 'Muhammad Shiraz' 'Abdullah Gani'] Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2…
Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger + 4 more
'Valentina Janev' 'Dea Pujić' 'Marko Jelić' 'Maria-Esther Vidal'] The goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security…
Ying-Chih Lin, Chin-Sheng Yu, Yen-Jen Lin
Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it…
Yang Liu, Rongbo Shen, Lu Zhou, Qingyu Xiao + 2 more
Advancements in high-throughput sequencing technologies and artificial intelligence offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of artificial intelligence technologies require automated…
Manoj Muniswamaiah
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review…
Bjørn Fjukstad, Vanessa Dumeaux, Karina Standahl Olsen, Michael Hallet + 2 more
As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an…
Michael C. Schatz
The last 20 years have been a remarkable era for biology and medicine. One of the most significant achievements has been the sequencing of the first human genomes, which has laid the foundation for profound insights into human genetics, the intricacies of regulation and development, and the forces of evolution.…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Yasset Perez-Riverol, Pablo Moreno
The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process involving multiple algorithms and tools. A wide variety of…
Gunther Eysenbach, Joseph Rudolf, Arriel Benis, Jacob McPadden + 10 more
'Thomas JS Durant' 'Dustin R Bunch' 'Andreas Coppi' 'Nathaniel Price' 'Kris Rodgerson' 'Charles J Torre Jr' 'William Byron' 'Allen L Hsiao' 'Harlan M Krumholz' 'Wade L Schulz'] Background Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine…
Authors not listed
Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have…
Onur Mutlu
Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. As a result, many key applications' performance, efficiency and scalability are bottlenecked by data movement. In this invited…
Longbing Cao
The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science…
Authors not listed
Untargeted metabolomics is a powerful approach for exploring the chemical diversity and dynamics of biological systems. However, the types of questions that can be addressed depend not only on experimental design but also on the data processing and analysis workflows employed, many of which require advanced…
Rosa Virginia Encinas Quille, Felipe Valencia de Almeida, Mauro Yuji Ohara, Pedro Luiz Pizzigatti Corrêa + 5 more
'Mauro Yuji Ohara' 'Pedro Luiz Pizzigatti Corrêa' 'Leandro Gomes de Freitas' 'Solange Nice Alves-Souza' 'Jorge Rady de Almeida Jr.' 'Maggie Davis' 'Giri Prakash'] Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on…
Jan Range, Colin Halupczok, Jens Lohmann, Neil Swainston + 6 more
EnzymeML is an XML–based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which…
Authors not listed
The nanosafety domain has seen significant advancements in data generation and sharing, yet challenges remain in ensuring data interoperability and reuse. This article focuses on developing a semantic interoperability framework for nanosafety data to maximize the FAIRness (Findability, Accessibility, Interoperability…
Rebecca Brunk, Kriti Shukla, Bryant Hutson, Yue Wang + 7 more
Genomic sequencing and other big biological data is unquestionably of paramount value, however the success in recruiting highly skilled individuals with diverse backgrounds has been limited. A main reason for this deficiency could be due to the lack of educational resources and early exposure to the field. With the…