AIMC Topic: Data Mining

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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Scientific reports
Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient machine learning (ML) model with robust performance to identify depression linked to heavy metal exposur...

Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.

JMIR cancer
BACKGROUND: Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can ...

Learning the rules of peptide self-assembly through data mining with large language models.

Science advances
Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition and exterior conditions on self-assembly, a systematic study consolidating the...

An automated classification pipeline for tables in pharmacokinetic literature.

Scientific reports
Pharmacokinetic (PK) models are essential for optimising drug candidate selection and dosing regimens in drug development. Preclinical and population PK models benefit from integrating prior knowledge from existing compounds. While tables in scientif...

Integrating data mining with transcranial focused ultrasound to refine neuralgia treatment strategies.

Journal of neuroscience methods
BACKGROUND: Neuralgia and other neuropathic pain are difficult to treat owing to their complicated etiology and a wide variety of responses to treatment. The novel neuromodulation technology transcranial focused ultrasound (tFUS) has intriguing impli...

Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents.

Accident; analysis and prevention
Fall-from-height (FFH) accidents remain a major source of workplace injuries and fatalities. Fall protection systems (FPS) are critical for preventing falls in the work-at-height (WAH) environment. However, challenges in designing and selecting effec...

Presenting a prediction model for HELLP syndrome through data mining.

BMC medical informatics and decision making
BACKGROUND: The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pr...

Using Generative AI to Extract Structured Information from Free Text Pathology Reports.

Journal of medical systems
Manually converting unstructured text pathology reports into structured pathology reports is very time-consuming and prone to errors. This study demonstrates the transformative potential of generative AI in automating the analysis of free-text pathol...

Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.

BMC medical informatics and decision making
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...