Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.

Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scientific terms. Despite recent efforts to learn term evolution from Knowledge Bases (KBs) for HG, the temporal information from multi-source KBs is still overlooked, which contains important, up-to-date knowledge.

Generating Biomedical Hypothesis With Spatiotemporal Transformers.

Generating biomedical hypotheses is a difficult task as it requires uncovering the implicit associations between massive scientific terms from a large body of published literature. A recent line of Hypothesis Generation (HG) approaches - temporal graph-based approaches - have shown great success in modeling temporal evolution of term-pair relationships. However, these approaches model the temporal evolution of each term or term-pair with Recurrent Neural Network (RNN) independently, which neglects the rich covariation among all terms or term-pairs while ignoring direct dependencies between any two timesteps in a temporal sequence.

Playing Extensive Games with Learning of Opponent's Cognition.

Decision-making is a basic component of agents' (e.g., intelligent sensors) behaviors, in which one's cognition plays a crucial role in the process and outcome.

Improving neural protein-protein interaction extraction with knowledge selection.

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts.

The Robust Classification Model Based on Combinatorial Features.

Analyzing the disease data from the view of combinatorial features may better characterize the disease phenotype. In this study, a novel method is proposed to construct feature combinations and a classification model (CFC-CM) by mining key feature relationships. CFC-CM iteratively tests for differences in the feature relationship between different groups.

A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.

Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PB-DSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks.

Proton magnetic resonance spectroscopy in the evaluation of infiltration zone of cerebral alveolar echinococcosis.

Background: Cerebral alveolar echinococcosis (CAE) grows infiltratively like a malignant tumor, causing great harm to the human body. It is possible to display mass lesions of CAE using various imaging systems, but regarding the infiltrating proliferation active regions, it is difficult to evaluate its actual range using conventional magnetic resonance imaging (cMRI). This research focused on proton magnetic resonance spectroscopy ((1)HMRS) techniques to find the mass and infiltration zone of CAE.