AIMC Topic: Machine Learning

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Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.

Environmental monitoring and assessment
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agr...

Graph-based machine learning model for weight prediction in protein-protein networks.

BMC bioinformatics
Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The...

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Scientific reports
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

JCI insight
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high di...

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

Acta neurologica Belgica
BACKGROUND: Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent strok...

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

Urologic oncology
OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data ...

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

Journal of immunotherapy (Hagerstown, Md. : 1997)
In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA- seq ) and to identify metastasis-related T cell marker genes...

Academic-related stressors predict depressive symptoms in graduate students: A machine learning study.

Behavioural brain research
BACKGROUND: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.

A machine-learning-based algorithm for bone marrow cell differential counting.

International journal of medical informatics
BACKGROUND: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed.

MultiThal-classifier, a machine learning-based multi-class model for thalassemia diagnosis and classification.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: The differential diagnosis between iron deficiency anemia (IDA) and thalassemia trait (TT) remains a significant clinical challenge. This study aimed to develop a machine learning-based multi-class model to differentiate among Microcytic-...