Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring [0.03%]
人工智能与公民科学结合助力生态监测
Eva C McClure,Michael Sievers,Christopher J Brown et al.
Eva C McClure et al.
The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible wi...
The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems [0.03%]
农食系统大数据国际农业研究磋商组计划：本体论实践社区
Elizabeth Arnaud,Marie-Angélique Laporte,Soonho Kim et al.
Elizabeth Arnaud et al.
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, la...
Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach [0.03%]
应对21世纪开放科学及其未来挑战的数据实验室方法学研究
Michael J Hollaway,Graham Dean,Gordon S Blair et al.
Michael J Hollaway et al.
In recent years, there has been a drive toward more open, cross-disciplinary science taking center stage. This has presented a number of challenges, including providing research platforms for collaborating scientists to explore big data, de...
Arthur Andrew Meahan MacDonald,Francis Banville,Timothée Poisot
Arthur Andrew Meahan MacDonald
Predicting the number of interactions among species in a food web is an important task. These trophic interactions underlie many ecological and evolutionary processes, ranging from biomass fluxes, ecosystem stability, resilience to extincti...
Sarah Callaghan
Sarah Callaghan
Yasemin Turkyilmaz-van der Velden,Nicolas Dintzner,Marta Teperek
Yasemin Turkyilmaz-van der Velden
Who hasn't yet heard about the debates on research reproducibility, or, perhaps even more, about the research reproducibility crisis? There have been numerous papers in the past several years discussing reproducibility issues in research. I...
Uncovering Effective Explanations for Interactive Genomic Data Analysis [0.03%]
发掘用于交互式基因组数据分析的有效解释方法
Silu Huang,Charles Blatti,Saurabh Sinha et al.
Silu Huang et al.
Better tools are needed to enable researchers to quickly identify and explore effective and interpretable feature-based explanations for discriminating multi-class genomic datasets, e.g., healthy versus diseased samples. We develop an inter...
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential [0.03%]
数字病理学中的生成对抗网络：趋势与未来潜力综述
Maximilian E Tschuchnig,Gertie J Oostingh,Michael Gadermayr
Maximilian E Tschuchnig
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic d...
Cellular State Transformations Using Deep Learning for Precision Medicine Applications [0.03%]
基于深度学习的细胞状态转换在精准医学中的应用
Colin Targonski,M Reed Bender,Benjamin T Shealy et al.
Colin Targonski et al.
We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcripto...
The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database [0.03%]
美国退伍军人事务精确诊断肿瘤学数据仓储中心：一个集临床、基因组和影像研究于一体的数据库
Danne C Elbers,Nathanael R Fillmore,Feng-Chi Sung et al.
Danne C Elbers et al.
The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). Data include longitudinal clinical data ...