A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.

Functional explication of genes is of great scientific value. However, conventional methods have challenges for those genes that may affect biological processes but are not annotated in public databases. Here, we developed a novel explainable gene ontology fingerprint (XGOF) method to automatically produce knowledge networks on biomedical literature in a given field which quantitatively characterizes the association between genes and ontologies.

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types.

Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types.

GIDB: a knowledge database for the automated curation and multidimensional analysis of molecular signatures in gastrointestinal cancer.

Gastrointestinal (GI) cancer is common, characterized by high mortality, and includes oesophagus, gastric, liver, bile duct, pancreas, rectal and colon cancers. The insufficient specificity and sensitivity of biomarkers is still a key clinical hindrance for GI cancer diagnosis and successful treatment. The emergence of `precision medicine', `basket trial' and `field cancerization' concepts calls for an urgent need and importance for the understanding of how organ system cancers occur at the molecular levels.

Identification of Salt Tolerance-related microRNAs and Their Targets in Maize ( L.) Using High-throughput Sequencing and Degradome Analysis.

To identify the known and novel microRNAs (miRNAs) and their targets that are involved in the response and adaptation of maize () to salt stress, miRNAs and their targets were identified by a combined analysis of the deep sequencing of small RNAs (sRNA) and degradome libraries. The identities were confirmed by a quantitative expression analysis with over 100 million raw reads of sRNA and degradome sequences. A total of 1040 previously known miRNAs were identified from four maize libraries, with 762 and 726 miRNAs derived from leaves and roots, respectively, and 448 miRNAs that were common between the leaves and roots.