AIMC Topic: Machine Learning

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Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Journal of computer-aided molecular design
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...

Machine learning assisted adjustment boosts efficiency of exact inference in randomized controlled trials.

Scientific reports
In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum's framework of exact tests in randomized experiments with covariate adju...

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy.

Scientific reports
Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths after trauma. Understanding the genetic underpinnings and molecular mechanisms of TIC is crucial for de...

Effectiveness of machine learning models in diagnosis of heart disease: a comparative study.

Scientific reports
The precise diagnosis of heart disease represents a significant obstacle within the medical field, demanding the implementation of advanced diagnostic instruments and methodologies. This article conducts an extensive examination of the efficacy of di...

Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.

Scientific reports
Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of...

Application of multimodal machine learning-based analysis for the biomethane yields of NaOH-pretreated biomass.

Scientific reports
This study investigated the impact of alkaline pretreatment on the biomethane yield of Xyris capensis experimentally and computationally using machine-learning (ML)-based techniques. Despite extensive studies on the anaerobic digestion of lignocellul...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Scientific reports
Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound...

Design and analysis of TwinCardio framework to detect and monitor cardiovascular diseases using digital twin and deep neural network.

Scientific reports
World Health Organization (WHO) estimates 17.9 million deaths globally every year due to Cardiovascular Disease or CVD, which includes an array of disorders of the heart and blood vessels, that includes coronary heart disease, cerebrovascular disease...

Predicting SARS-CoV-2-specific CD4 and CD8 T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models.

Clinical and experimental medicine
The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune responses to guide vaccination strategies. Although neutralizing antibodies (NAbs) have garnered increasing ...

Electronic-Nose Technology for Lung Cancer Detection: A Non-Invasive Diagnostic Revolution.

Lung
BACKGROUND: Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, primarily due to late-stage diagnosis and the absence of effective early detection methods.