AIMC Topic: Machine Learning

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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images.

Scientific reports
Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment....

Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.

Scientific reports
This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component an...

Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.

Scientific reports
This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with ...

Human mobility is well described by closed-form gravity-like models learned automatically from data.

Nature communications
Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the mod...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Immunologic research
Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging outcomes in a subset of HCC patients. The main challenges in checkpoint immunotherapy for HCC are to e...

Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.

Environmental monitoring and assessment
Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning id...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

PLoS medicine
BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML]...

M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.

IEEE transactions on medical imaging
Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging...