AIMC Topic: Machine Learning

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Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials.

The Journal of chemical physics
Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material pro...

Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.

The Journal of clinical endocrinology and metabolism
CONTEXT: Although elevated parathyroid hormone (PTH) levels are associated with higher mortality risks, the evidence is limited as to when PTH is expected to be elevated and thus should be measured among the general population.

Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Yi chuan = Hereditas
Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic clas...

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...

Machine learning alternative to systems biology should not solely depend on data.

Briefings in bioinformatics
In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered...

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 ...

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Briefings in bioinformatics
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graph...

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...

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.

GigaScience
BACKGROUND: A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we condu...