AIMC Topic: Machine Learning

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Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning.

Artificial intelligence in medicine
Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is...

Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

Tropical animal health and production
This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. T...

High-accuracy protein structures by combining machine-learning with physics-based refinement.

Proteins
Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolution...

A genetic programming-based approach to identify potential inhibitors of serine protease of .

Future medicinal chemistry
We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of () and the discovery of new inhibitors as drug candidates. The experimental dataset of serine protease inhibit...

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

NMR in biomedicine
Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of ...

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Journal of computer-aided molecular design
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for...

Deep-UV excitation fluorescence microscopy for detection of lymph node metastasis using deep neural network.

Scientific reports
Deep-UV (DUV) excitation fluorescence microscopy has potential to provide rapid diagnosis with simple technique comparing to conventional histopathology based on hematoxylin and eosin (H&E) staining. We established a fluorescent staining protocol for...

Machine Learning to Quantitate Neutrophil NETosis.

Scientific reports
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate...

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...