Single Cell RNA Sequencing and Data Analysis DOI
Mukunda Goswami,

Ashikha Kitchlu,

Bibhu Prasad Behera

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Exploring Safety Research Progress and Prospects for the Sustainable Development of Resilient Cities DOI Creative Commons

Bingrui Tong,

Hui Liu, Jun‐Jie Zhu

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(3), P. 505 - 505

Published: Feb. 6, 2025

In the context of global climate change and accelerated urbanization, construction resilient safe cities has become key to addressing both natural human-made disasters. This literature review systematically analyzes relevant data from city studies published in SCIE SSCI databases 2000 2023, focusing on risk safety perspectives. Using bibliometric tools, spatial–temporal distribution, collaboration networks, knowledge foundations are examined, revealing current state, core topics, emerging trends research. The findings indicate that contemporary research primarily focuses disaster response, infrastructure resilience, community engagement, application big technologies, reflecting a trend toward interdisciplinary integration. not only provides comprehensive theoretical framework for academic but also offers data-driven decision support governments. results highlight directions future research, contributing enhancement urban resilience managing complex risks promoting sustainable development globally.

Language: Английский

Citations

0

Mapping Data-Driven Research Impact Science: The Role of Machine Learning and Artificial Intelligence DOI Open Access
Mudassar Hassan Arsalan, Omar Mubin, Abdullah Al Mahmud

et al.

Metrics, Journal Year: 2025, Volume and Issue: 2(2), P. 5 - 5

Published: April 2, 2025

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body literature this domain necessitates consolidation to provide a comprehensive understanding applications multidimensional assessment. This study bridges gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, advanced topic modelling using BERTopic, analyse curated corpus 1608 articles. Guided three core questions, investigates how ML AI enhance evaluation, identifies dominant outlines future directions. The findings underscore potential augment traditional indicators uncovering latent patterns collaboration networks, institutional influence, knowledge dissemination. particular, scalability semantic depth BERTopic thematic extraction, combined with visualisation capabilities tools such as CiteSpace VOSviewer, novel insights into dynamic interplay contributions across dimensions. Theoretically, extends scientometric discourse integrating computational techniques reconfiguring established paradigms for assessing contributions. Practically, it provides actionable researchers, institutions, policymakers, enabling enhanced strategic decision-making visibility impactful research. By proposing robust, data-driven framework, lays groundwork holistic equitable addressing its academic, societal, economic

Language: Английский

Citations

0

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis DOI
Oscar E. Ospina,

Roberto Manjarres-Betancur,

Guillermo González-Calderón

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 2, 2024

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential often underutilized due to the advanced data analysis programming skills required. To address this, we present spatialGE, web application that simplifies of ST data. The spatialGE provides user-friendly interface guides users without expertise through various pipelines, including quality control, normalization, domain detection, phenotyping, multiple spatial analyses. It also enables comparative among samples supports technologies. We demonstrate utility in studying tumor microenvironment melanoma brain metastasis Merkel cell carcinoma. Our results highlight ability identify gene expression patterns enrichments, providing valuable insights into democratizing wider scientific community.

Language: Английский

Citations

1

Single Cell RNA Sequencing and Data Analysis DOI
Mukunda Goswami,

Ashikha Kitchlu,

Bibhu Prasad Behera

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

0