A Connected Convolutional Neutral Network Protocol for Design of Two-Dimensional Materials Based on Modified Graphdiyne.

In materials science, doping plays a crucial role in manipulating the electronic properties of materials. Conventional screening via a trial-and-error strategy is challenging owing to the enormous chemical space. We proposed a connected convolutional neutral network (CCNN) for quick screening of boron nitrogen (B-N) codoped graphdiyne in terms of band gap.

Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma.

Background: The overactivation of NF-κB signaling is a key hallmark for the pathogenesis of extranodal natural killer/T cell lymphoma (ENKTL), a very aggressive subtype of non-Hodgkin's lymphoma yet with rather limited control strategies. Previously, we found that the dysregulated exportin-1 (also known as CRM1) is mainly responsible for tumor cells to evade apoptosis and promote tumor-associated pathways such as NF-κB signaling.

Methods: Herein we reported the discovery and biological evaluation of a potent small molecule CRM1 inhibitor, LFS-1107.

HMNPPID-human malignant neoplasm protein-protein interaction database.

Background: Protein-protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available.

Neural network-based approaches for biomedical relation classification: A review.

The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to automatically detect and classify biomedical relations, which has great benefits for various biomedical research and applications. In the past decade, significant progress has been made in biomedical relation classification.

Incorporating User Generated Content for Drug Drug Interaction Extraction Based on Full Attention Mechanism.

It is crucial for doctors to fully understand the interaction between drugs in prescriptions, especially when a patient takes multiple medications at the same time during treatment. The purpose of drug drug interaction (DDI) extraction is to automatically obtain the interaction between drugs from biomedical literature. Current state-of-the-art approaches for DDI extraction task are based on artificial intelligence and natural language processing.

Protein complexes identification based on go attributed network embedding.

Background: Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework.

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

Background: Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated.

An attention-based effective neural model for drug-drug interactions extraction.

Background: Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory.

Drug drug interaction extraction from biomedical literature using syntax convolutional neural network.

Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.

A graph kernel based on context vectors for extracting drug-drug interactions.

The clinical recognition of drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can't yet adequately express long and complicated sentences.

PPIExtractor: a protein interaction extraction and visualization system for biomedical literature.

Protein-protein interactions (PPIs) play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interaction information manually.

Extracting drug-drug interaction from the biomedical literature using a stacked generalization-based approach.

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature.