AIMC Topic: Databases, Genetic

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Ensemble Neural Networks (ENN): A gradient-free stochastic method.

Neural networks : the official journal of the International Neural Network Society
In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the e...

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.

International journal of molecular sciences
Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNA...

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...

Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification.

Computers in biology and medicine
In cancer classification, gene selection is one of the most important bioinformatics related topics. The selection of genes can be considered to be a variable selection problem, which aims to find a small subset of genes that has the most discriminat...

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

Scientific reports
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome th...

Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.

BMC bioinformatics
BACKGROUND: Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interaction...

"Plutchik": artificial intelligence chatbot for searching NCBI databases.

Journal of the Medical Library Association : JMLA
As genetic testing gains ground in medicine, the ability to search across the suite of biomedical and clinical care databases offered through the National Library of Medicine/National Center for Biotechnology Information (NCBI)-such as PubMed, GENE, ...

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

BMC bioinformatics
BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.

WEADE: A workflow for enrichment analysis and data exploration.

PloS one
Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis...