MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively.

Loss of NDUFS1 promotes gastric cancer progression by activating the mitochondrial ROS-HIF1α-FBLN5 signaling pathway.

Background: Recent studies suggested that NDUFS1 has an important role in human cancers; however, the effects of NDUFS1 on gastric cancer (GC) are still not fully understood.

Methods: We confirmed that NDUFS1 is downregulated in GC cells through western blot immunohistochemistry and bioinformation analysis. The effect of NDUFS1 on GC was studied by CCK-8, colony formation, transwell assay in vitro and Mouse xenograft assay in vivo.

Novel Approach to Enriching Glycosylated RNAs: Specific Capture of GlycoRNAs via Solid-Phase Chemistry.

Ribonuclease (RNA) modifications can alter cellular function and lead to differential immune responses by acting as discriminators between RNAs from different phyla. RNA glycosylation has recently been observed at the cell surface, and its dysregulation in disease may change RNA functions. However, determining which RNA substrates can be glycosylated remains to be explored.

Translational informatics for human microbiota: data resources, models and applications.

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

Background: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy.

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

Background: Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker.

ING4 suppresses hepatocellular carcinoma via a NF-κB/miR-155/FOXO3a signaling axis.

The tumor suppressor ING4 has been shown to be reduced in human HCC. The alteration of ING4 contributes to HCC progression. However, its effect in HCC and the potential mechanism is largely unclear.

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges.

Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications.

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science.

Translational biomedical informatics in the cloud: present and future.

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration.