AIMC Topic: Machine Learning

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Provable Boolean interaction recovery from tree ensemble obtained via random forests.

Proceedings of the National Academy of Sciences of the United States of America
Random Forests (RFs) are at the cutting edge of supervised machine learning in terms of prediction performance, especially in genomics. Iterative RFs (iRFs) use a tree ensemble from iteratively modified RFs to obtain predictive and stable nonlinear o...

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.

BMC geriatrics
BACKGROUND: Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can red...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

Frontiers in immunology
High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed depending on fluorescence hybridization (FISH), an expensive and laborious analysis. In order to identify H...

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models.

Computational intelligence and neuroscience
Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the ...

Churn prediction in telecommunication industry using kernel Support Vector Machines.

PloS one
In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an ad...

Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver.

International journal of computer assisted radiology and surgery
PURPOSE: The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are sc...

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Nature biotechnology
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions....

A classification for complex imbalanced data in disease screening and early diagnosis.

Statistics in medicine
Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention und...

An annotated corpus of clinical trial publications supporting schema-based relational information extraction.

Journal of biomedical semantics
BACKGROUND: The evidence-based medicine paradigm requires the ability to aggregate and compare outcomes of interventions across different trials. This can be facilitated and partially automatized by information extraction systems. In order to support...

Learning Representations Using RNN Encoder-Decoder for Edge Security Control.

Computational intelligence and neuroscience
Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of mac...