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Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.

Journal of biomedical informatics
Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of c...

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.

PLoS computational biology
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequenc...

Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

IEEE/ACM transactions on computational biology and bioinformatics
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher or...

Annotation of gene product function from high-throughput studies using the Gene Ontology.

Database : the journal of biological databases and curation
High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The repres...

iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes.

BMC bioinformatics
BACKGROUND: A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants ...

Identifying mouse developmental essential genes using machine learning.

Disease models & mechanisms
The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies o...

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available...

An Interpretable ICU Mortality Prediction Model Based on Logistic Regression and Recurrent Neural Networks with LSTM units.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Most existing studies used logistic regression to establish scoring systems to predict intensive care unit (ICU) mortality. Machine learning-based approaches can achieve higher prediction accuracy but, unlike the scoring systems, frequently cannot pr...

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...