AIMC Topic: Machine Learning

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Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices.

Sensors (Basel, Switzerland)
Accurate crop yield forecasting is essential in the food industry's decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, howe...

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes.

Scientific reports
Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to r...

Using machine learning to detect the differential usage of novel gene isoforms.

BMC bioinformatics
BACKGROUND: Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can...

Radioactive hot-spot localisation and identification using deep learning.

Journal of radiological protection : official journal of the Society for Radiological Protection
The detection of radioactive hot-spots and the identification of the radionuclides present have been a challenge for the security sector, especially in situations involving chemical, biological, radiological, nuclear and explosive threats, as well as...

Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm.

PloS one
BACKGROUND: Early detection and prediction of cisplatin-induced acute kidney injury (Cis-AKI) are essential for the management of patients on chemotherapy with cisplatin. This study aimed to evaluate the performance of a prediction model for Cis-AKI.

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics.

Journal of ultrasound
AIMS: We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

Learning protein fitness models from evolutionary and assay-labeled data.

Nature biotechnology
Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent ...

Annual Research Review: Translational machine learning for child and adolescent psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the li...

Transfer inhibitory potency prediction to binary classification: A model only needs a small training set.

Computer methods and programs in biomedicine
One of the most laborious for drug discovery is to select compounds from a library for experimental evaluation. Hence, we propose a machine learning model only needs to be trained on a small dataset to predict the inhibition constant (Ki) and half ma...