AIMC Topic: Phenotype

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Deep learning in mammography and breast histology, an overview and future trends.

Medical image analysis
Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promisin...

Matching biomedical ontologies based on formal concept analysis.

Journal of biomedical semantics
BACKGROUND: The goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed mathematical model for analy...

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction.

Translational psychiatry
Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nomin...

Informatics and machine learning to define the phenotype.

Expert review of molecular diagnostics
For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained...

Functional Categorization of Disease Genes Based on Spectral Graph Theory and Integrated Biological Knowledge.

Interdisciplinary sciences, computational life sciences
Interaction of multiple genetic variants is a major challenge in the development of effective treatment strategies for complex disorders. Identifying the most promising genes enhances the understanding of the underlying mechanisms of the disease, whi...

Machine learning in cardiovascular medicine: are we there yet?

Heart (British Cardiac Society)
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing sev...

WorMachine: machine learning-based phenotypic analysis tool for worms.

BMC biology
BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software th...

Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems.

Database : the journal of biological databases and curation
Natural language descriptions of organismal phenotypes, a principal object of study in biology, are abundant in the biological literature. Expressing these phenotypes as logical statements using ontologies would enable large-scale analysis on phenoty...

Variants in GNPTAB, GNPTG and NAGPA genes are associated with stutterers.

Gene
Non-syndromic stuttering is a neurodevelopmental disorder characterized by disruptions in normal flow of speech in the form of repetition, prolongation and involuntary halts. Previously, mutations with more severe effects on GNPTAB and GNPTG have bee...

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

Molecules (Basel, Switzerland)
Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE)...