In recent years, a huge amount of biomedical data has been generated by high throughput sequencing facilities. These data include, but are not limited to, genomic sequences, transcriptome profiles, non-coding RNA profiles, epigenetics profiles, and single cell RNA-seq profiles. Such datasets are usually large and incomprehensible to humans. Moreover, a careful multiple-steps analysis with computational methods is needed to extract meaningful patterns.
We propose this Research Topic to cover recent work that reports new bioinformatics methods, new results of all aspects of life, and new databases related to nucleic acids and protein information. We welcome manuscripts related to the following aspects:
• how to efficiently align millions of high throughput sequencing reads to the genomes?
• how to identify meaning sequence motifs in nucleic acid or amino acid sequences?
• how to identify genes with unique expression patterns in specific biological conditions, such as disease?
• how to identify meaningful groups in experimental samples?
• How to accurately predict diseases based on genomic and/or transcriptomic profiles?
• what are the sequence motifs of a unique set of genes or proteins?
• what are splicing patterns in specific diseases?
• what are the uniquely expressed genes or non-coding RNAs in diseases?
• what is the molecular mechanism underlying the diseases?
• and any other interesting molecular patterns in special biological conditions, such as human diseases.
Research Articles and Reviews dedicated to all aspects of employing machine learning methods to retrieve interesting biomedical patterns from big biomedical data are welcome.
Finally, systematic organization and interesting presentation of big biomedical data in web-based databases are also considered. Meaningful biomedical patterns include, but are not limited to: uniquely expressed gene clusters in diseases, gene or proteins correlated to specific phenotypes, sequence motifs of proteins or nucleic acids, motifs, or pathways in biological networks, splicing patterns in specific biological conditions, and epigenetic patterns in specific biological conditions.
In recent years, a huge amount of biomedical data has been generated by high throughput sequencing facilities. These data include, but are not limited to, genomic sequences, transcriptome profiles, non-coding RNA profiles, epigenetics profiles, and single cell RNA-seq profiles. Such datasets are usually large and incomprehensible to humans. Moreover, a careful multiple-steps analysis with computational methods is needed to extract meaningful patterns.
We propose this Research Topic to cover recent work that reports new bioinformatics methods, new results of all aspects of life, and new databases related to nucleic acids and protein information. We welcome manuscripts related to the following aspects:
• how to efficiently align millions of high throughput sequencing reads to the genomes?
• how to identify meaning sequence motifs in nucleic acid or amino acid sequences?
• how to identify genes with unique expression patterns in specific biological conditions, such as disease?
• how to identify meaningful groups in experimental samples?
• How to accurately predict diseases based on genomic and/or transcriptomic profiles?
• what are the sequence motifs of a unique set of genes or proteins?
• what are splicing patterns in specific diseases?
• what are the uniquely expressed genes or non-coding RNAs in diseases?
• what is the molecular mechanism underlying the diseases?
• and any other interesting molecular patterns in special biological conditions, such as human diseases.
Research Articles and Reviews dedicated to all aspects of employing machine learning methods to retrieve interesting biomedical patterns from big biomedical data are welcome.
Finally, systematic organization and interesting presentation of big biomedical data in web-based databases are also considered. Meaningful biomedical patterns include, but are not limited to: uniquely expressed gene clusters in diseases, gene or proteins correlated to specific phenotypes, sequence motifs of proteins or nucleic acids, motifs, or pathways in biological networks, splicing patterns in specific biological conditions, and epigenetic patterns in specific biological conditions.
