AIMC Topic: Machine Learning

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Multi-Omics Analysis of the virulence factors and designing of next-generation multi-epitopes Vaccines against Rickettsia prowazekii: a computer-aided vaccine designing approach.

Journal of computer-aided molecular design
Rickettsia is a genus of bacteria that are obligate intracellular parasites and are responsible for the febrile diseases known collectively as Rickettsioses. The emergence of antibiotic resistance is an escalating concern and thus developing a vaccin...

Retrospective cohort study of infection and risk stratification using 6-year UBT data.

Frontiers in public health
BACKGROUND: () infection is a major global health concern, linked to gastric cancer and metabolic disorders. Despite its widespread prevalence, accurate risk stratification remains challenging. This study aims to develop a machine learning (ML)-base...

MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data.

Journal of proteome research
MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compat...

MobNas ensembled model for breast cancer prediction.

Scientific reports
Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning ...

Personalized learning in hybrid education.

Scientific reports
The process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied l...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

Journal of neuroengineering and rehabilitation
BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying ...

Novel Computational Approaches in the Discovery and Identification of Bioactive Peptides: A Bioinformatics Perspective.

Journal of agricultural and food chemistry
Bioactive peptides are protein molecules known for their specific biological functions, offering promising applications across various fields including medicine, food, and cosmetics. Traditional approaches to the investigation of bioactive peptides t...

Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance.

Molecules (Basel, Switzerland)
Antimicrobial resistance (AMR) is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug-targe...

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.

BMC psychiatry
Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizo...

Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques.

Scientific reports
Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but thei...