AIMC Topic: Machine Learning

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Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning.

The journal of physical chemistry letters
Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be us...

Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space.

Molecules (Basel, Switzerland)
Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to p...

Deciphering tumour tissue organization by 3D electron microscopy and machine learning.

Communications biology
Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organizatio...

Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.

Scientific reports
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was ...

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Scientific reports
Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the...

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.

Scientific reports
To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT ima...

AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity.

BMC bioinformatics
BACKGROUND: More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods b...

Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models.

Journal of healthcare engineering
Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, co...

Machine learning for classification of postoperative patient status using standardized medical data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analy...

Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning.

Sensors (Basel, Switzerland)
As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the ...