AIMC Topic: Machine Learning

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Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

Current medicinal chemistry
The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through spe...

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial.

Knowledge Graphs and Their Applications in Drug Discovery.

Methods in molecular biology (Clifton, N.J.)
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing o...

Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.

Combinatorial chemistry & high throughput screening
BACKGROUND: To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs ...

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Current drug discovery technologies
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its...

Artificial Intelligence in ADME Property Prediction.

Methods in molecular biology (Clifton, N.J.)
Absorption, distribution, metabolism, excretion (ADME) are key properties of a small molecule that govern pharmacokinetic profiles and impact its efficacy and safety. Computational methods such as machine learning and artificial intelligence have gai...

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.

Current drug delivery
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intel...

Machine Learning-derived Multi-omics Prognostic Signature of Pyroptosis-related lncRNA with Regard to ZKSCAN2-DT and Tumor Immune Infiltration in Colorectal Cancer.

Combinatorial chemistry & high throughput screening
BACKGROUND: Colorectal cancer (CRC) has become the most prevalent gastrointestinal malignant tumor, ranking third (10.2%) in incidence and second (9.2%) in death among all malignancies globally. The most common histological subtype of CRC is colon ad...

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence.

Current computer-aided drug design
BACKGROUND: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic featur...