A question-answer generation system for an asynchronous distance learning platform.

Distance learning frees the learning process from spatial constraints. Each mode of distance learning, including synchronous and asynchronous learning, has disadvantages. In synchronous learning, students have network bandwidth and noise concerns, but in asynchronous learning, they have fewer opportunities for engagement, such as asking questions. The difficulties associated with asynchronous learning make it difficult for teachers to determine whether students comprehend the course material. Motivated students will consistently participate in a course and prepare for classroom activities if teachers ask questions and communicate with them during class. As an aid to distance education, we want to automatically generate a sequence of questions based on asynchronous learning content. In this study, we will also generate multiple-choice questions for students to answer and teachers to easily correct. The asynchronous distance teaching-question generation (ADT-QG) model, which includes Sentences-BERT (SBERT) in the model architecture to generate questions from sentences with a higher degree of similarity, is proposed in this work. With the Wiki corpus generation option, it is anticipated that the Transfer Text-to-Text Transformer (T5) model will generate more fluent questions and be more aligned with the instructional topic. The results indicate that the questions created by the ADT-QG model suggested in this work have good fluency and clarity indicators, showing that the questions generated by the ADT-QG model are of a certain quality and relevant to the curriculum.

Automatic content curation of news events.

With the rapid development of the internet, a large amount of online news has brought readers a variety of information. Some important events last for some time as the event develops or the topic spreads. When readers want to catch up on the details of a specific news event, most of them use a search engine to collect news and understand the whole story. It usually takes readers a considerable amount of time to sort out the causes and effects of the event. The general method of online news provision aggregates and organizes the content of news articles from a large number of events and presents the content to readers. Most of this type of information is manually organized. To solve these problems, this study proposes an automated method of news curation. First, we extract the topics from the event data set and use word sequences to find the sequence of topic transfer through a hidden Markov model. Second, we calculate the strength of the topic and the variation in the strength to detect important time points during the development of the news event. Finally, a concise summary is generated at each time point. This paper combines two characteristics, chronology and summary, to design a curation method that can effectively help readers quickly grasp the context of a news event. The experimental results show that the method has good performance in each module, such as the detection of the important phases of events and the creation of the news summary.

A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature.

Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.

Using positive and negative patterns to extract information from journal articles regarding the regulation of a target gene by a transcription factor.

Gene regulation research concerns the regulatory relationship between transcription factors (TFs) and their target genes (TGenes). Due to the rapid acceleration of biological research, it is impractical for biologists to read all of the relevant literature and manually extract all of the information about the regulatory relationships between a TF and its TGenes. This paper proposes a method utilizing negative and positive textual patterns to extract regulatory information regarding certain TF-TGene pairs, which provides insightful information to biologists and saves them time from excessive literature reading. We hypothesized that the negative patterns could be used for filtering and that the system would mainly rely on the positive patterns to mine the regulatory TF-TGene relationships from the text. We also examined whether WordNet could be utilized to improve the pattern recognition performance. The results show that the negative pattern should be used for initial filtering, and then the positive patterns can extract information related to gene regulation. Moreover, WordNet seems to have little effect on the performance when extracting gene regulations.

Using unsupervised patterns to extract gene regulation relationships for network construction.

BACKGROUND: The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively. CONCLUSIONS/SIGNIFICANCE: Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/.

Inference of transcriptional regulatory network by bootstrapping patterns.

MOTIVATION: Transcriptional regulatory networks, which consist of linkages between transcription factors (TF) and target genes (TGene), control the expression of a genome and play important roles in all aspects of an organism's life cycle. Accurate prediction of transcriptional regulatory networks is critical in providing useful information for biologists to determine what to do next. Currently, there is a substantial amount of fragmented gene regulation information described in the medical literature. However, current related text analysis methods designed to identify protein-protein interactions are not entirely suitable for finding transcriptional regulatory networks. RESULT: In this article, we propose an automatic regulatory network inference method that uses bootstrapping of description patterns to predict the relationship between a TF and its TGenes. The proposed method differs from other regulatory network generators in that it makes use of both positive and negative patterns for different vector combinations in a sentence. Moreover, the positive pattern learning process can be fully automatic. Furthermore, patterns for active and passive voice sentences are learned separately. The experiments use 609 HIF-1 expert-tagged articles from PubMed as the gold standard. The results show that the proposed method can automatically generate a predicted regulatory network for a transcription factor. Our system achieves an F-measure of 72.60%. AVAILABILITY: The software, training/test datasets and learned patterns are available at http://140.116.99.138/∼hcw0901/PubMedSearch.php.

Prediction of EST functional relationships via literature mining with user-specified parameters.

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Comparison of transcripts in Phalaenopsis bellina and Phalaenopsis equestris (Orchidaceae) flowers to deduce monoterpene biosynthesis pathway.

BACKGROUND: Floral scent is one of the important strategies for ensuring fertilization and for determining seed or fruit set. Research on plant scents has hampered mainly by the invisibility of this character, its dynamic nature, and complex mixtures of components that are present in very small quantities. Most progress in scent research, as in other areas of plant biology, has come from the use of molecular and biochemical techniques. Although volatile components have been identified in several orchid species, the biosynthetic pathways of orchid flower fragrance are far from understood. We investigated how flower fragrance was generated in certain Phalaenopsis orchids by determining the chemical components of the floral scent, identifying floral expressed-sequence-tags (ESTs), and deducing the pathways of floral scent biosynthesis in Phalaneopsis bellina by bioinformatics analysis. RESULTS: The main chemical components in the P. bellina flower were shown by gas chromatography-mass spectrometry to be monoterpenoids, benzenoids and phenylpropanoids. The set of floral scent producing enzymes in the biosynthetic pathway from glyceraldehyde-3-phosphate (G3P) to geraniol and linalool were recognized through data mining of the P. bellina floral EST database (dbEST). Transcripts preferentially expressed in P. bellina were distinguished by comparing the scent floral dbEST to that of a scentless species, P. equestris, and included those encoding lipoxygenase, epimerase, diacylglycerol kinase and geranyl diphosphate synthase. In addition, EST filtering results showed that transcripts encoding signal transduction and Myb transcription factors and methyltransferase, in addition to those for scent biosynthesis, were detected by in silico hybridization of the P. bellina unigene database against those of the scentless species, rice and Arabidopsis. Altogether, we pinpointed 66% of the biosynthetic steps from G3P to geraniol, linalool and their derivatives. CONCLUSION: This systems biology program combined chemical analysis, genomics and bioinformatics to elucidate the scent biosynthesis pathway and identify the relevant genes. It integrates the forward and reverse genetic approaches to knowledge discovery by which researchers can study non-model plants.