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Genetic Predisposition to Disease

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C-PUGP: A cluster-based positive unlabeled learning method for disease gene prediction and prioritization.

Computational biology and chemistry
Disease gene detection is an important stage in the understanding disease processes and treatment. Some candidate disease genes are identified using many machine learning methods Although there are some differences in these methods including feature ...

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.

Genomics
The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computat...

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...

Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine.

Interdisciplinary sciences, computational life sciences
Essential hypertension (EH) has become a major chronic disease around the world. To build a risk-predicting model for EH can help to interpose people's lifestyle and dietary habit to decrease the risk of getting EH. In this study, we constructed a EH...

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method.

Proceedings of the National Academy of Sciences of the United States of America
Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual's susceptibility to ca...

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...

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways.

Scientific reports
The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making ...

AutismKB 2.0: a knowledgebase for the genetic evidence of autism spectrum disorder.

Database : the journal of biological databases and curation
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong genetic contributions. To provide a comprehensive resource for the genetic evidence of ASD, we have updated the Autism KnowledgeBase (AutismKB) to version 2.0. Autism...

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...