AIMC Topic: Machine Learning

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Everything is connected: Graph neural networks.

Current opinion in structural biology
In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent...

CauRuler: Causal irredundant association rule miner for complex patient trajectory modelling.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Discovering causal associations between variables is one of the main goals of clinical trials, with the ultimate aim of identifying the causes of specific health status. Prior knowledge of causal paths could help ensure pat...

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.

Journal of chemical information and modeling
Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors we...

Comparative Studies on Resampling Techniques in Machine Learning and Deep Learning Models for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland)
The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success of machine learning and deep learning methods in accurately predicting DTIs plays a huge role in drug discovery. However, when dealing with learning algo...

Building an automated, machine learning-enabled platform for predicting post-operative complications.

Physiological measurement
. In 2019, the University of Florida College of Medicine launched thealgorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record.. This project was developed in parallel with our ...

Construction of a new smooth support vector machine model and its application in heart disease diagnosis.

PloS one
Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to fin...

Exploring the expressiveness of abstract metabolic networks.

PloS one
Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified re...

Deception detection with machine learning: A systematic review and statistical analysis.

PloS one
Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, ...

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.

Computational intelligence and neuroscience
Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. Howeve...