AIMC Topic: Algorithms

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ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Briefings in bioinformatics
Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological te...

Artificial intelligence-driven prediction of multiple drug interactions.

Briefings in bioinformatics
When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides ...

FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.

Briefings in bioinformatics
Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In r...

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Briefings in bioinformatics
Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of t...

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction.

Briefings in bioinformatics
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especial...

Multi-modality artificial intelligence in digital pathology.

Briefings in bioinformatics
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the ...

Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models.

The Journal of chemical physics
Despite the modern advances in the available computational resources, the length and time scales of the physical systems that can be studied in full atomic detail, via molecular simulations, are still limited. To overcome such limitations, coarse-gra...

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to real...

Measurement method of aero-engine rotor concentricity and perpendicularity based on deep belief neural network.

The Review of scientific instruments
When implementing the traditional assembly method, the rotor is affected by machining errors. The morphology of the rotor is complex, and the machining error of the rotors at all levels are transmitted step by step through the stop mating surface, wh...

Prediction of pathological staging and grading of renal clear cell carcinoma based on deep learning algorithms.

The Journal of international medical research
OBJECTIVE: Deep learning algorithms were used to develop a model for predicting the staging and grading of renal clear cell carcinoma to inform clinicians' treatment plans.