Vision Transformer with Key-Select Routing Attention for Single Image Dehazing.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Label-free proteome quantification and evaluation.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

ConSIG: consistent discovery of molecular signature from OMIC data.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics.

The Exploration of a Machine Learning Approach for the Assessment of Learning Styles changes.

NOREVA: normalization and evaluation of MS-based metabolomics data.