Biology: The big challenges of big data

By V. Marx. 2013

1,133 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, background): “A in machine learning (1) and data science (2) have promised a renaissance in the analysis and understanding of complex data, extracting patterns in vast multimodal data that are beyond the ability of humans to grasp.”
Deep Learning for Health Informatics (2017, background): “geneous data such as gene alleles, proteins occurrences, and environmental factors [126].”
SupportNet: solving catastrophic forgetting in class incremental learning with support data (2018, background): “In the past decade, we have witnessed the dramatic increase in the amount of the genomic data (Marx, 2013), protein sequence data (UniProt, 2007) and the emergence of various databases (Zou et al. , 2015).”
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015, background): “While the amount of health-associated data and the number of large scales global projects increases, integrative analysis of this data is proving to be problematic [27].”
Freshwater biomonitoring in the Information Age (2017, methodology): “In contrast to other more established fields in biology (Marx 2013), bioassess ment is just beginning to face the problems associated with massive datasets.”
Machine Learning and Data Mining Methods in Diabetes Research (2017, background): “Introduction Significant advances in biotechnology and more spec ifically high-throughput sequencing result incessantly in an easy and inexpe nsive data production, thereby ushering the science of applied biology into the ar ea of big data [1,2].”
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017, background): “Big data in health and biology to tackle the challenges in new models [61], [62] is becoming significant One study introduced two uses of mHealth, which gathers electronic medical records”
Factors influencing big data decision-making quality (2017, background): “Marx (2013) proposes five steps; problem definition, data searching, data transformation, data entity resolution, answer the query/solve the problem.”
Machine learning in critical care: state-of-the-art and a sepsis case study (2018, background): “We are witnessing an epochal change in Biology as it quickly evolves from a wet labora-tory-centered science to a data-driven endeavour [1], a process that can be interpreted as an example of the pervasive Big Data paradigm [2].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, background): “meaningful conclusion from these data is a huge challenge [9].”
Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science (2016, background): “Experiences from the biomedical sectors shows that in particular smaller research groups lack the required expertise and funding to build the required processing and analysis infrastructures (Bishop 2013; Goecks et al. 2010; Lynch 2008; Marx 2013; Noor et al. 2015; Sitto and Presser 2015).”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “The enormous amount of data being generated in the biological field, which was once thought to be a big challenge [99], actually makes deep learning very suitable for biological analysis.”
Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania (2013, background): “Therefore biodiversity data has to be regarded as Big Data, which Improved access to integrated biodiversity data for science, practice, and policy... 55 requires flexible information systems (Marx 2013).”