AIMC Topic: Machine Learning

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HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure.

Protein science : a publication of the Protein Society
In the past, several methods have been developed for predicting conformational B-cell epitopes in antigens that are not specific to any host. Our primary analysis of antibody-antigen complexes indicated a need to develop host-specific B-cell epitopes...

A large language model for predicting neurotoxic peptides and neurotoxins.

Protein science : a publication of the Protein Society
The accurate prediction of neurotoxicity in peptides and proteins is essential for the safety evaluation of therapeutic proteins and genetically modified (GM) organisms. Existing tools, including our earlier method NTxPred, typically use a single pre...

Artificial intelligence applications in the screening and classification of glioblastoma.

Journal of neurosurgical sciences
Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation ...

Discrimination of Klebsiella pneumoniae and Klebsiella quasipneumoniae by MALDI-TOF Mass Spectrometry Coupled With Machine Learning.

MicrobiologyOpen
Klebsiella species, including Klebsiella pneumoniae and Klebsiella quasipneumoniae, present significant challenges in clinical microbiology due to their genetic similarity, which complicates accurate species identification using established methods, ...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

European journal of public health
Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (MASLD), atherogenic dyslipidaemia (ATD), and chronic kidney disease (CKD). The absence of reliable t...

Multiomics and Machine Learning Identify Immunometabolic Biomarkers for Active Tuberculosis Diagnosis Against Nontuberculous Mycobacteria and Latent Tuberculosis Infection.

Journal of proteome research
This study utilized multiomics combined with a comprehensive machine learning-based predictive modeling approach to identify, validate, and prioritize circulating immunometabolic biomarkers in distinguishing tuberculosis (TB) from nontuberculous myco...

Harnessing computational technologies to facilitate antibody-drug conjugate development.

Nature chemical biology
Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known chemical agents with the specificity of monoclonal antibodies, thereby maximizing efficacy while minim...

Optimizing surgical efficiency: predicting case duration of common general surgery procedures using machine learning.

Surgical endoscopy
BACKGROUND: Accurate prediction of surgical duration is critical to optimizing use of operating room resources. Currently, cases are scheduled using subjective estimates of length by surgeons, relying heavily on prior experience. This study aims to d...

Elucidating environmental fate and toxicological mechanisms of ultrashort- and short-chain PFAS: Integrating machine learning, molecular modeling, and experimental validation.

Journal of environmental management
Per- and polyfluoroalkyl substances (PFAS), due to their recalcitrance, toxicity, and widespread environmental distribution, have emerged as a critical public health concern. The fate of ultrashort- and short-chain PFAS in the heterogeneous environme...

AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.

Annals of neurology
Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understa...