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Data Mining

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Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes.

Computers in biology and medicine
Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare fa...

Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records.

International journal of medical informatics
BACKGROUND: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient sa...

Mining core information by evaluating semantic importance for unpaired image captioning.

Neural networks : the official journal of the International Neural Network Society
Recently, exciting progress has been made in the research of supervised image captioning. However, manually annotated image-annotation pair data is difficult and expensive to obtain. Therefore, unpaired image captioning becomes an emerging challenge....

A data science roadmap for open science organizations engaged in early-stage drug discovery.

Nature communications
The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is...

OpenChemIE: An Information Extraction Toolkit for Chemistry Literature.

Journal of chemical information and modeling
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly inv...

Analyzing variation of water inflow to inland lakes under climate change: Integrating deep learning and time series data mining.

Environmental research
The alarming depletion of global inland lakes in recent decades makes it essential to predict water inflow from rivers to lakes (WIRL) trend and unveil the dominant influencing driver, particularly in the context of climate change. The raw time serie...

Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment.

Neural networks : the official journal of the International Neural Network Society
Multi-Modal Entity Alignment (MMEA), aiming to discover matching entity pairs on two multi-modal knowledge graphs (MMKGs), is an essential task in knowledge graph fusion. Through mining feature information of MMKGs, entities are aligned to tackle the...

Enabling CMF estimation in data-constrained scenarios: A semantic-encoding knowledge mining model.

Accident; analysis and prevention
Availability of more accurate Crash Modification Factors (CMFs) is crucial for evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scena...

Precision Drug Repurposing: A Deep Learning Toolkit for Identifying 34 Hyperpigmentation-Associated Genes and Optimizing Treatment Selection.

Annals of plastic surgery
BACKGROUND: Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offe...

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Neural networks : the official journal of the International Neural Network Society
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detect...