Synopsis of Social media discussions
The discussions highlight that many users find CuMiDa valuable for benchmarking machine learning techniques, with comments such as 'a highly curated resource' and 'a potential game changer.' The tone varies from appreciation to lively debate about its methodological rigor and practical utility, demonstrating genuine interest and recognition of its potential impact.
Agreement
Moderate agreementMost discussions recognize the value of CuMiDa as a comprehensive resource for cancer research and benchmarking machine learning models.
Interest
Moderate level of interestParticipants show moderate curiosity, with some expressing enthusiasm about the potential applications and usefulness of the database.
Engagement
High engagementMany responses delve into specific features like data quality and benchmarking methods, indicating deep involvement.
Impact
Moderate level of impactThe talks suggest the database could significantly influence ongoing research and method development in cancer studies.
Social Mentions
YouTube
3 Videos
1 Posts
Metrics
Video Views
156
Total Likes
6
Extended Reach
1,342
Social Features
4
Timeline: Posts about article
Top Social Media Posts
Int Soc Biocuration
#biocuration https://t.co/becd9VNzkT CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research.
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#biocuration https://t.co/becd9VNzkT CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research.
view full postFebruary 23, 2019
Abstract Synopsis
- CuMiDa is a specialized microarray database containing 78 carefully selected human data sets from over 30,000 experiments, designed specifically for evaluating machine learning methods in cancer research.
- The data sets undergo rigorous processing, including background correction, normalization, quality checks, and manual editing to ensure high quality and accuracy.
- CuMiDa is intended as a benchmarking tool, offering baseline results using principal component analysis, tSNE, and various machine learning techniques to assist researchers in testing and comparing new approaches in cancer gene expression studies.]
Int Soc Biocuration
@biocurator (Twitter)