Attention-based High-order Feature Interactions to Enhance the Recommender System for Web-based Knowledge-Sharing Servic

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service

Establishment of quantitative nested-PCR of Abelson interactor 1 transcript variant-11

Abelson interactor 1 (ABI1), which presents 18 Transcript Variants (TSV), plays an important role in CRC metastasis. Different ABI1-TSVs play synergistic or antagonistic roles in the same pathophysiological events. ABI1 Transcript Variant-11 (ABI1-TSV-11) functionally promotes lymph node metastasis of left-sided colorectal cancer (LsCC) and is an independent molecular marker to evaluate the prognosis of patients with LsCC. However, there is still lack of a quick and accurate method to detect the expression of ABI-TSV-11, distinguishing ABI1-TSV-11 from other 17 TSVs. To establish a rapid method specific for ABI1-TSV-11detection, we developed a quantitative nested-PCR method composed of pre-amplification regular PCR using ABI1 universal primer pair and the followed Real Time (RT)-qPCR using ABI1-TSV-11 specific primer pair spanning exon-exon junction. ABI1-TSV-11-overexpressed SW480 and LoVo cell lines were used to verify the quantitative nested-PCR assay, and the sequencing data was used to evaluate the accuracy of ABI1-TSV-11 quantitative nested-PCR assay. The detection limit was 5.24×104 copies/ml. ABI1-TSV-11 quantitative nested-PCR provides a new technical means for the detection of ABI1-TSV-11

Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services

From Ideal to Reality: Segmentation, Annotation, and Recommendation, the Vital Trajectory of Intelligent Micro Learning

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning

Refinement and Augmentation for Data in Micro Learning Activity with an Evolutionary Rule Generators

Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account both rare and negative association rules (NARs). These optimized rules are further applied in refining and augmenting data denoting learners’ behaviors in open learning into a low‐dimensional, descriptive and interpretable form. The performance of rule discovery and data processing have been empirically evaluated using genuine open learning data

Evaluation of a Cloud-based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners

In this paper, we present an online computation approach implemented in a cloud-based system to assist open education resource (OER) providers and instructors dealing with the sparsity of data in micro OER recommendation

Identification of AP002498.1 and LINC01871 as prognostic biomarkers and therapeutic targets for distant metastasis of colorectal adenocarcinoma

Abstract Background Increasing evidence suggests that lncRNA (Long non‐coding RNA, lncRNA)‐mediated ceRNA (competing endogenous RNA, ceRNA) networks are involved in the occurrence and progression of colorectal cancer (CRC). However, the roles of the lncRNA–miRNA–mRNA ceRNA network in distant metastasis of CRC are still unclear. Methods In this study, we constructed a specific ceRNA network to identify potential biomarkers and therapeutic targets for distant metastasis of CRC. Specifically, RNA‐Seq data from The Cancer Genome Atlas (TCGA) were used to screen for differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) related to metastasis. After validation and selection by qRT–PCR and univariate and multivariate analysis of the metastasis‐ and prognosis‐related lncRNAs, the regulated microRNAs (miRNAs) and coexpressed mRNAs were used to construct a ceRNA network for distant metastasis of CRC. Results Two key distant metastasis‐related DElncRNAs, AP002498.1 and LINC01871, were identified by univariate and multivariate analysis in combination with analyses of clinical data and expression levels. Furthermore, lncRNA‐associated ceRNA subnetworks were constructed from the predicted miRNAs and 13 coexpressed DEmRNAs (SERPINA1, ITLN1, REG4, L1TD1, IGFALS, MUC5B, CIITA, CXCL9, CXCL10, GBP4, GNLY, IDO1, and NOS2). The AP002498.1‐ and LINC01871‐associated ceRNA subnetworks regulated the expression of the target genes SERPINA1 and MUC5B and GNLY, respectively, through the associated miRNAs. Conclusion The DElncRNA AP002498.1 and the LINC01871/miR‐4644 and miR‐185‐5p/GNLY axes were identified as being closely associated with distant metastasis and could represent independent prognostic biomarkers or therapeutic targets in colorectal adenocarcinoma