AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Databases, Genetic

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MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

Journal of translational medicine
BACKGROUND: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been pai...

Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.

Communications biology
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to...

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility.

CNS neuroscience & therapeutics
AIMS: As one of the most fundamental questions in modern science, "what causes schizophrenia (SZ)" remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies i...

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

International journal of molecular sciences
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networ...

Visualizing complex feature interactions and feature sharing in genomic deep neural networks.

BMC bioinformatics
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...

PhenPath: a tool for characterizing biological functions underlying different phenotypes.

BMC genomics
BACKGROUND: Many diseases are associated with complex patterns of symptoms and phenotypic manifestations. Parsimonious explanations aim at reconciling the multiplicity of phenotypic traits with the perturbation of one or few biological functions. For...

Heuristic filter feature selection methods for medical datasets.

Genomics
Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset. The significance of gene selection is in high dimensional datasets in which the number of samples and features are low and high respectively. The major goa...

Cancer classification and pathway discovery using non-negative matrix factorization.

Journal of biomedical informatics
OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks.

BMC bioinformatics
BACKGROUND: Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in...