AIMC Topic: Neural Networks, Computer

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Biological network analysis with deep learning.

Briefings in bioinformatics Mar 22, 2021
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the ...
Humans Neural Networks, Computer Algorithms Drug Discovery Deep Learning Gene Regulatory Networks Computational Biology
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Identifying drug-target interactions based on graph convolutional network and deep neural network.

Briefings in bioinformatics Mar 22, 2021
Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. Howev...
Neural Networks, Computer Humans Algorithms Deep Learning Drug Delivery Systems
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Application of deep learning methods in biological networks.

Briefings in bioinformatics Mar 22, 2021
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological system...
Genotype Proteins Drug Interactions Microbiota Data Mining Drug Discovery Deep Learning Protein Interaction Maps Algorithms Phenotype Drug Delivery Systems Humans Neural Networks, Computer
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Deep learning for biological age estimation.

Briefings in bioinformatics Mar 22, 2021
Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data minin...
Aging Exercise Deep Learning Neural Networks, Computer Biomarkers Electronic Health Records Computational Biology Epigenesis, Genetic Anthropometry Humans
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Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Briefings in bioinformatics Mar 22, 2021
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus ...
Humans Neural Networks, Computer Drug Discovery Drug-Related Side Effects and Adverse Reactions Deep Learning Drug Repositioning Drug Interactions
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Integrating multi-network topology for gene function prediction using deep neural networks.

Briefings in bioinformatics Mar 22, 2021
MOTIVATION: The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and pro...
Algorithms Deep Learning Humans Gene Regulatory Networks Neural Networks, Computer Yeasts Genes, Fungal Molecular Sequence Annotation
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DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

Briefings in bioinformatics Mar 22, 2021
The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods per...
Deep Learning RNA, Messenger RNA, Long Noncoding Open Reading Frames Humans Neural Networks, Computer Animals
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Biological applications of knowledge graph embedding models.

Briefings in bioinformatics Mar 22, 2021
Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and ...
Machine Learning Humans Algorithms Neural Networks, Computer Computational Biology Drug Interactions
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Verification and comparison of three prediction models of ischemic stroke in young adults based on the back propagation neural networks.

Medicine Mar 19, 2021
This work aims to explore risk factors for ischemic stroke in young adults and analyze the Traditional Vascular Risk Factors Model based on age, hypertension, diabetes, smoking history, and drinking history. Further, the Lipid Metabolism Model was an...
Adult Age Factors Female Smoking Ischemic Stroke Lipoproteins Risk Assessment Risk Factors Male Alcohol Drinking Diabetes Mellitus, Type 2 Predictive Value of Tests Factor Analysis, Statistical Young Adult Adolescent Clinical Decision Rules Middle Aged Hypertension Area Under Curve ROC Curve Humans Kidney Function Tests Neural Networks, Computer Acute Kidney Injury
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Importance-aware personalized learning for early risk prediction using static and dynamic health data.

Journal of the American Medical Informatics Association : JAMIA Mar 18, 2021
OBJECTIVE: Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, import...
Models, Theoretical Data Mining Datasets as Topic Peptic Ulcer Hemorrhage Prognosis Risk Assessment Deep Learning Humans Neural Networks, Computer Precision Medicine
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