AIMC Topic: Data Curation

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MaterialBrain: High-Performance Material Synthesis Extraction via Human-AI-Curated Few-Shot Large Language Models.

Journal of chemical information and modeling
The extraction of the metal-organic framework synthesis route from the literature has been crucial for the rational MOFs design with desirable functionality. The recent advent of large language models (LLMs) provides a disruptive new solution to this...

Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative Study.

JMIR medical informatics
BACKGROUND: Manual review of electronic health records for clinical research is labor-intensive and prone to reviewer-dependent variations. Large language models (LLMs) offer potential for automated clinical data extraction; however, their feasibilit...

A Curated and Re-annotated Peripheral Blood Cell Dataset Integrating Four Public Resources.

Scientific data
We present TXL-PBC, a curated and re-annotated peripheral blood cell dataset constructed by integrating four publicly available resources: Blood Cell Count and Detection (BCCD), Blood Cell Detection Dataset (BCDD), Peripheral Blood Cells (PBC), and R...

LR-COBRAS: A logic reasoning-driven interactive medical image data annotation algorithm.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The volume of image data generated in the medical field is continuously increasing. Manual annotation is both costly and prone to human error. Additionally, deep learning-based medical image algorithms rely on large, accurately annotated training dat...

Utilizing Large language models to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation.

BMC medical research methodology
BACKGROUND: Large language models (LLMs) like ChatGPT showed great potential in aiding medical research. A heavy workload in filtering records is needed during the research process of evidence-based medicine, especially meta-analysis. However, few st...

Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.

Human genomics
BACKGROUND: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these a...

Machine learning tools match physician accuracy in multilingual text annotation.

Scientific reports
In the medical field, text annotation involves categorizing clinical and biomedical texts with specific medical categories, enhancing the organization and interpretation of large volumes of unstructured data. This process is crucial for developing to...

Partial Annotation Learning for Biomedical Entity Recognition.

IEEE journal of biomedical and health informatics
Named Entity Recognition (NER) is a key task to support biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining high-quality expert annotated data is laborious and expensive, leading to the development of automatic approaches ...

Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.

JMIR human factors
BACKGROUND: Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning techniques applied to routinely collected data can enhance clinical tools and improve patient outcom...

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures.

Journal of molecular biology
Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures ...