Ten quick tips for avoiding pitfalls in multi-omics data integration analyses
Data are the most important elements of bioinformatics: Computational analysis of
bioinformatics data, in fact, can help researchers infer new knowledge about biology …
bioinformatics data, in fact, can help researchers infer new knowledge about biology …
Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data
BM Pucher, OA Zeleznik… - Briefings in …, 2019 - academic.oup.com
Integrative analysis aims to identify the driving factors of a biological process by the joint
exploration of data from multiple cellular levels. The volume of omics data produced is …
exploration of data from multiple cellular levels. The volume of omics data produced is …
[HTML][HTML] Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data
This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse
grouping problem. It has been applied for the classification of an unbalanced database of …
grouping problem. It has been applied for the classification of an unbalanced database of …
nRC: non-coding RNA Classifier based on structural features
Abstract Motivation Non-coding RNA (ncRNA) are small non-coding sequences involved in
gene expression regulation of many biological processes and diseases. The recent …
gene expression regulation of many biological processes and diseases. The recent …
Machine learning-based state-of-the-art methods for the classification of rna-seq data
Ribonucleic acid sequencing (RNA-Seq) measures the expression levels of several
transcripts simultaneously. The readings can be gene, exon, or other regions of interest …
transcripts simultaneously. The readings can be gene, exon, or other regions of interest …
A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis
Background and objective The cause of the Alzheimer's disease is poorly understood and to
date no treatment to stop or reverse its progression has been discovered. In developed …
date no treatment to stop or reverse its progression has been discovered. In developed …
[HTML][HTML] Machine learning and related approaches in transcriptomics
Y Cheng, SM Xu, K Santucci, G Lindner… - … and Biophysical Research …, 2024 - Elsevier
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic
analytical pipeline. However, recent developments in transcriptome profiling technologies …
analytical pipeline. However, recent developments in transcriptome profiling technologies …
Classification of large DNA methylation datasets for identifying cancer drivers
DNA methylation is a well-studied genetic modification crucial to regulate the functioning of
the genome. Its alterations play an important role in tumorigenesis and tumor-suppression …
the genome. Its alterations play an important role in tumorigenesis and tumor-suppression …
A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data
Background The ability to sequence the transcriptomes of single cells using single-cell RNA-
seq sequencing technologies presents a shift in the scientific paradigm where scientists …
seq sequencing technologies presents a shift in the scientific paradigm where scientists …
Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction
Abstract Background In the Next Generation Sequencing (NGS) era a large amount of
biological data is being sequenced, analyzed, and stored in many public databases, whose …
biological data is being sequenced, analyzed, and stored in many public databases, whose …