AIMC Topic: Computational Biology

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A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks.

BMC bioinformatics
BACKGROUND: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, prot...

Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction.

Computational and mathematical methods in medicine
Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the hig...

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images.

Computational and mathematical methods in medicine
Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early det...

Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery.

Computational and mathematical methods in medicine
Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the ...

Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models.

PloS one
PURPOSE: Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigati...

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning.

Genes
Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA-protein interactions through experimental methods...

Predicting subcellular location of protein with evolution information and sequence-based deep learning.

BMC bioinformatics
BACKGROUND: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the p...

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation.

PLoS computational biology
MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates...

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Computational and mathematical methods in medicine
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...

Context-aware multi-token concept recognition of biological entities.

BMC bioinformatics
BACKGROUND: Concept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and plays an essential role in the field of bioinformatics. However, the conventional dictionary-based ...