Feature selection may improve deep neural networks for the bioinformatics problems
Z Chen, M Pang, Z Zhao, S Li, R Miao, Y Zhang… - …, 2020 - academic.oup.com
Motivation Deep neural network (DNN) algorithms were utilized in predicting various
biomedical phenotypes recently, and demonstrated very good prediction performances …
biomedical phenotypes recently, and demonstrated very good prediction performances …
A deep neural network model using random forest to extract feature representation for gene expression data classification
In predictive model development, gene expression data is associated with the unique
challenge that the number of samples (n) is much smaller than the amount of features (p) …
challenge that the number of samples (n) is much smaller than the amount of features (p) …
Biological interpretation of deep neural network for phenotype prediction based on gene expression
B Hanczar, F Zehraoui, T Issa, M Arles - BMC bioinformatics, 2020 - Springer
Background The use of predictive gene signatures to assist clinical decision is becoming
more and more important. Deep learning has a huge potential in the prediction of phenotype …
more and more important. Deep learning has a huge potential in the prediction of phenotype …
Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data
V Bourgeais, F Zehraoui, M Ben Hamdoune… - BMC …, 2021 - Springer
Background With the rapid advancement of genomic sequencing techniques, massive
production of gene expression data is becoming possible, which prompts the development …
production of gene expression data is becoming possible, which prompts the development …
IntegratedMRF: random forest-based framework for integrating prediction from different data types
IntegratedMRF is an open-source R implementation for integrating drug response
predictions from various genomic characterizations using univariate or multivariate random …
predictions from various genomic characterizations using univariate or multivariate random …
DeepFeature: feature selection in nonimage data using convolutional neural network
Artificial intelligence methods offer exciting new capabilities for the discovery of biological
mechanisms from raw data because they are able to detect vastly more complex patterns of …
mechanisms from raw data because they are able to detect vastly more complex patterns of …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
S He, F Guo, Q Zou - Current Bioinformatics, 2020 - ingentaconnect.com
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Fast prediction of protein methylation sites using a sequence-based feature selection technique
Protein methylation, an important post-translational modification, plays crucial roles in many
cellular processes. The accurate prediction of protein methylation sites is fundamentally …
cellular processes. The accurate prediction of protein methylation sites is fundamentally …
Convolutional neural network models for cancer type prediction based on gene expression
Background Precise prediction of cancer types is vital for cancer diagnosis and therapy.
Through a predictive model, important cancer marker genes can be inferred. Several studies …
Through a predictive model, important cancer marker genes can be inferred. Several studies …
Detection of transcription factors binding to methylated DNA by deep recurrent neural network
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It
is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from …
is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from …