AIMC Topic: Models, Genetic

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Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting mod...

Superiority of artificial neural networks for a genetic classification procedure.

Genetics and molecular research : GMR
The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant function...

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Genetics and molecular research : GMR
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning tech...

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis.

Human heredity
Genome-wide association studies have revealed a vast amount of common loci associated to human complex diseases. Still, a large proportion of heritability remains unexplained. The extent to which rare genetic variants (RVs) are able to explain a rele...

Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.

BioMed research international
MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can...

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

Journal of theoretical biology
The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the p...

Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.

Mutation research
Cancer is one of the most life-threatening diseases and mutations in several genes are the vital cause in tumorigenesis. Protein kinases play essential roles in cancer progression and specifically, epidermal growth factor receptor (EGFR) is an import...

Genome Modeling System: A Knowledge Management Platform for Genomics.

PLoS computational biology
In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled...

Machine learning applications in genetics and genomics.

Nature reviews. Genetics
The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning ap...

Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

Cognitive science
In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively...