Ten quick tips for sequence-based prediction of protein properties using machine learning | PLOS Computational Biology
The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, the authors have noticed several recurring issues, which make some reported findings hard to understand and replicate. They suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins. Here, the authors aim to bridge this gap for developers of such methods.
Reference panel guided topological structure annotation of Hi-C data | Nature Communications
Accurately annotating topological structures (e.g., loops and topologically associating domains) from Hi-C data is critical for understanding the role of 3D genome organization in gene regulation. Here, researchers present RefHiC, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate topological structure annotation from a given study sample.
Visual Omics: A web-based platform for omics data analysis and visualization with rich graph-tuning capabilities | Bioinformatics
With the continuous development of high-throughput sequencing technology, bioinformatic analysis of omics data plays an increasingly important role in life science research. Researchers have created Visual Omics, an online tool for omics data analysis and scientific chart editing. Visual Omics integrates multiple omics analyses which include differential expression analysis, enrichment analysis, protein domain prediction and protein-protein interaction analysis with extensive graph presentations.