AI Medical Compendium Topic:
Computational Biology

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High-performing cross-dataset machine learning reveals robust microbiota alteration in secondary apical periodontitis.

Frontiers in cellular and infection microbiology
Multiple research groups have consistently underscored the intricate interplay between the microbiome and apical periodontitis. However, the presence of variability in experimental design and quantitative assessment have added a layer of complexity, ...

Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.

Aging
Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to identify key circadian rhythm genes (CRGs) in melanoma and explore their potential as diagnostic and prog...

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

International journal of molecular sciences
Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (E...

Deep learning for predicting 16S rRNA gene copy number.

Scientific reports
Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different co...

Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential.

Scientific reports
Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolut...

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

PLoS computational biology
Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep ...

Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.

BMC bioinformatics
BACKGROUND: Compared to traditional supervised machine learning approaches employing fully labeled samples, positive-unlabeled (PU) learning techniques aim to classify "unlabeled" samples based on a smaller proportion of known positive examples. This...

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained ...

Precision Drug Repurposing: A Deep Learning Toolkit for Identifying 34 Hyperpigmentation-Associated Genes and Optimizing Treatment Selection.

Annals of plastic surgery
BACKGROUND: Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offe...

EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.

BMC bioinformatics
BACKGROUND: Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of eff...