AIMC Topic: Computational Biology

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Major pathophysiological changes in pulmonary disease provided a molecular insight based on deep learning approach.

Scientific reports
The outburst of pulmonary disorders among the society has shown the devastating effect of undergoing a delay in diagnosis and treatment. Sometimes the traditional methods in detecting and treating the airway disease fail to cure efficiently due to a ...

Combining knowledge distillation and neural networks to predict protein secondary structure.

Scientific reports
The secondary structure of a protein serves as the foundation for constructing its three-dimensional (3D) structure, which in turn is critical for determining its function and role in biological processes. Therefore, accurately predicting secondary s...

Enhancing pedagogical practices with Artificial Neural Networks in the age of AI to engage the next generation in Biomathematics.

Bulletin of mathematical biology
In this work we present a C-MATH-NN framework that extends a C-MATH framework that was developed in recent years to include prediction using artificial neural networks (NN) in a way that is engaging, interdisciplinary and collaborative to help equip ...

Exploring potential associations and biomarkers linked polycystic ovarian syndrome with atherosclerosis via comprehensive bioinformatics analysis, machine learning, and animal experiments.

Functional & integrative genomics
Polycystic ovary syndrome (PCOS), a common endocrine condition affecting multiple systems, is tied to atherosclerosis (AS) progression among reproductive-aged women. The present study aimed to explore the underlying associations and uncover potential...

Utility of the continuous spectrum formed by pathological states in characterizing disease properties.

NPJ systems biology and applications
Understanding diseases as the result of continuous transitions from a healthy system is more realistic than understanding them as discrete states. Here, we designed the spectrum formation approach (SFA), a machine learning-based method that extracts ...

A cross-sectional and bioinformatics-based analysis: perirenal fat thickness as a superior predictor of kidney stone disease.

Lipids in health and disease
BACKGROUND: Kidney stone disease (KSD) is a growing global health concern, with obesity (OB) as a major risk factor linked to metabolic dysfunction and chronic inflammation. Although the common method for evaluating OB is body mass index (BMI), it is...

The effect of taxonomic, host-dependent features and sample bias on virus host prediction using machine learning and short sequence k-mers.

Scientific reports
Metaviromic studies of potential emerging infection reservoirs led to discovery of many novel viruses. Since metaviromes contain viruses from target host, its food or other sources, fast and robust approaches are needed to predict hosts of unknown vi...

Screening of hub genes and immunocytes related to tendon injury based on bioinformatics and machine learning models.

Scientific reports
Tendon injury is a common and challenging clinical problem, and its healing process involves complex cellular and biological factors. Therefore, this study aims to reveal the mechanism of tendon healing and provide theoretical basis for clinical trea...

Environmental exposure to perfluorooctane sulfonate and its role in esophageal cancer progression: a comprehensive bioinformatics and experimental study.

Scientific reports
Esophageal cancer (ESCA) is a significant malignancy with rising global incidence rates and considerable impacts on patient survival and quality of life. Current diagnostic and therapeutic strategies face limitations, necessitating research into its ...