24 papers · ranked by Valyu relevance
Kornelia Batko, Andrzej Ślęzak
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the…
Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine, Hashim Ibrahim Bisallah
'Hashim Ibrahim Bisallah'] Abstract—The term, 'Big Data' has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. Big Data is still a novel concept, and in the following literature we intend to elaborate it in a palpable fashion. It commences with the…
Blend Berisha, Endrit Mëziu, Isak Shabani
Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot…
Shohel Sayeed, Abu Fuad Ahmad, Tan Choo Peng
The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities…
Wilson C. Hsieh, Ziqian Bi, Keyu Chen, Benji Peng + 22 more
Management Authors: ['Wilson C. Hsieh' 'Ziqian Bi' 'Keyu Chen' 'Benji Peng' 'Sen Zhang' 'Jiawei Xu' 'Jinlang Wang' 'Caitlyn Heqi Yin' 'Yichao Zhang' 'Pohsun Feng' 'Yizhu Wen' 'Tianyang Wang' 'Ming Li' 'Chia Xin Liang' 'Jin‐Tao Ren' 'Qian Niu' 'Silin Chen' 'Lingzhi Yan' 'Han Xu' 'H. Eric Tseng' 'Xinyuan Song' 'Bowen…
Rania Mkhinini Gahar, Olfa Arfaoui, Minyar Sassi Hidri
The new age of digital growth has marked all fields. This technological evolution has impacted data flows which have witnessed a rapid expansion over the last decade that makes the data traditional processing unable to catch up with the rapid flow of massive data. In this context, the implementation of a big data…
Daniele Piovani, Stefanos Bonovas
The term Big Data is used to describe extremely large datasets that are complex, multi-dimensional, unstructured, and heterogeneous and that are accumulating rapidly and may be analyzed with appropriate informatic and statistical methodologies to reveal patterns, trends, and associations [1]. In medical and healthcare…
Rogério Vieira Rossi, Kechi Hirama, Eduardo Franco
— The paradigm of Big Data has been established as a solid field of studies in many areas such as healthcare, science, transport, education, government services, among others. Despite widely discussed, there is no agreed definition about the paradigm although there are many concepts proposed by the academy and…
Leticia Leone Lauricella, Paulo Manuel Pêgo-Fernandes
“Information is the oil of the 21st century, and analytics is the combustion engine” said Peter Sondergaard, senior vice president of Gartner Research. The more information we have, the more likely we are to find correlations that are not obvious to the eye and that can completely change the way we think or act. We are…
Sulaiman Khan, Habib Ullah Khan, Shah Nazir
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract…
Rania Mkhinini Gahar, Olfa Arfaoui, Minyar Sassi Hidri
—To succeed in a Big Data strategy, you have to arm yourself with a wide range of data skills and best practices. This strategy can result in an impressive asset that can streamline operational costs, reduce time to market, and enable the creation of new products. However, several Big Data challenges may take place in…
Ugochukwu Orji, Ezugwu Obianuju, Modesta Ezema, Chikodili Ugwuishiwu + 2 more
'Elochukwu Ukwandu' 'Uchechukwu Agomuo'] The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw…
Asif Adil, Namrata Bhattacharya, Mohammed Asger
As the field of single-cell genomics continues to develop, the generation of large-scale scRNA-seq datasets has become more prevalent. While these datasets offer tremendous potential for shedding light on the complex biology of individual cells, the sheer volume of data presents significant challenges for management…
Chih Chuan Shih, Jieqi Chen, Ai Shan Lee, Nicolas Bertin + 34 more
Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital…
Yasin El Abiead, Michael Strobel, Thomas Payne, Eoin Fahy + 13 more
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, we've developed pan-repository universal identifiers and harmonized cross-repository metadata. This novel ecosystem…
Matthias Scheffler, Stefan Bauer, Peter Benner, Tristan Bereau + 57 more
Matthias Scheffler 1 , Stefan Bauer 2 , Peter Benner 3 , Tristan Bereau 4 , Volker Blum 5 , Mario Boley 6 , Christian Carbogno 7 , C. Richard A. Catlow 8 , Gerhard Dehm 9 , Sebastian Eibl 10 , Ralph Ernstorfer 11 , Ádám Fekete 12 , Lucas Foppa 1 , Peter Fratzl 13 , Christoph Freysoldt 9 , Baptiste Gault 9 , Luca M.…
Matthias Scheffler
Matthias Scheffler 1 , Stefan Bauer 2 , Peter Benner 3 , Tristan Bereau 4 , Volker Blum 5 , Mario Boley 6 , Christian Carbogno 7 , C. Richard A. Catlow 8 , Gerhard Dehm 9 , Sebastian Eibl 10 , Ralph Ernstorfer 11 , Ádám Fekete 12 , Lucas Foppa 1 , Peter Fratzl 13 , Christoph Freysoldt 9 , Baptiste Gault 9 , Luca M.…
Jaka Kokošar, Cagatay Turkay, Luka Avsec, Miha Štajdohar + 1 more
We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients’ survival rates and the onset of…
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…
Ricardo Stefani
The use of data science, artificial intelligence, and big data in the field of chemistry has recently grown to speed up the discovery of new materials, drugs, and synthetic substances and the identification of automated compounds. Machine learning and data science are commonly used in organic chemistry to predict…
Anton Nekrutenko, Danielle Callan, Marius Van Den Beek, Dannon Baker + 9 more
The analytical landscape of pathogen research is often fragmented, hindering transparency and reproducibility due to diverse genomic data sources, numerous software tools, and suboptimal integration methods. Here we introduce BRC-analytics, a novel browser-based environment that unifies authoritative sources of genomic…
Tanvi S. Patel, Daxesh P. Patel, Mallika Sanyal, Pranav S. Shrivastav
In the present work, we examined the outcomes and accuracy of the Support vector machine (SVM) and the Naive Bayes algorithms on a dataset, to predict whether the patient has heart disease or not, and the patient’s survival prediction status. The machine learning procedures were developed using the clinically validated…
Dimitar Georgiev, Simon Vilms Pedersen, Ruoxiao Xie, Álvaro Fernández-Galiana + 2 more
Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…