Multi-modal knowledge graph completion (MMKGC) leverages visual, textual, and structural information to predict missing entities in knowledge graphs. However, existing approaches predominantly rely on early fusion strategies that project heterogeneous modalities into unified representations, diminishing modality-specific discriminative capabilities and failing to fully exploit inter-modal complementarity. Unlike prior fusion-based models that merge all modalities into a single latent space, our approach treats each modality as an independent reasoning expert and integrates their complementary outputs through an interpretable ensemble mechanism. To address these limitations, we propose the Multi-Branch Ensemble with Cross-Modal Fusion (MBE-CF) framework, which preserves modality-specific characteristics through independent reasoning pathways while capturing cross-modal interactions via dedicated fusion mechanisms. Our approach introduces three key innovations: (1) a multi-modal attention fusion (MMAF) module that employs multi-head self-attention mechanisms to capture cross-modal interactions and obtain robust fused multi-modal representations, (2) a context triple encoder (CTE) that processes head-relation-tail sequences through modality-specific Transformer encoders to inject relational context into entity representations, and (3) an Ensemble Inference (EI) approach that uses independent Tucker decoders for structural, visual, textual, and multi-modal branches, aggregates per-branch scores at inference, and optimizes during training a Decision Fusion objective that sums branch-specific head and tail losses. The framework maintains architectural symmetry across modalities while facilitating comprehensive integration of heterogeneous reasoning patterns to leverage the complementary strengths of different information sources. Extensive experiments on three benchmark datasets (DB15K, MKG-W, and MKG-Y) demonstrate that MBE-CF achieves state-of-the-art performance, outperforming 9 baseline methods across multiple evaluation metrics.

Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. Naive solutions such as prediction-only or Predict-then-Optimize approaches suffer from goal mismatch. Additionally, the introduction of the SOTA time series forecasting model inevitably introduces additional uncertainty in the predicted result. To solve both problems mentioned above, we introduce a Risk-aware Time-Series Predict-and-Allocate (RTS-PnO) framework, which holds no prior assumption on the forecasting models. Such a framework contains three features: (i) end-to-end training with objective alignment measurement(ii) adaptive forecasting uncertainty calibration, and (iii) agnostic towards forecasting models. The evaluation of RTS-PnO is conducted over both online and offline experiments. For offline experiments, eight datasets from three categories of financial applications are used: Currency, Stock, and Cryptos. RTS-PnO consistently outperforms other competitive baselines. The online experiment is conducted on the Cross-Border Payment business at FiT, Tencent, and an 8.4% decrease in regret is witnessed when compared with the product-line approach. The code for the offline experiment is available.

Prescription opioids are powerful pain-reducing medications. Thousands of articles that focus on prescription opioid use (POU) and its associated medical disorders have been published. However, it is time-consuming and labor-intensive to extract and understand the information of all POU-related published articles. In this study, we applied the well-adapted topic modeling method, Latent Dirichlet Allocation (LDA), to perform text mining on POU-related literature. We have collected six large academic abstract datasets by searching PubMed using the Medical Subject Headings (MeSH): prescription opioid, codeine, morphine, hydrocodone, oxycodone, and methadone. We then applied topic modeling to identify topics and analyze topic similarities/differences in these six datasets. Word clouds and histograms were used to depict the distribution of vocabularies over each topic in which the most prevalent words conveyed a topic’s meaning. TreeMap and trend analysis were performed to fingerprint abstracts and explore the prevalent topic dynamics in the POU-related literature. Results showed the ability of topic modeling as a computational tool to segregate a vast quantity of articles into different themes that provide a systematic literature overview. The LDA topics recaptured the search keywords in PubMed and revealed further relevant themes by comparison analysis between different datasets.

Objective: To classify causal associations among drugs and adverse events by the identified drug safety signals from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Material and Methods: FAERS reports were collected for a period between 2004 and 2014. Empirical Bayes Geometric Mean was applied to model associations between drugs and adverse events. Identified signals were evaluated using Reporting Ratio values and Chi-Square test. Based on the identified drug-associated adverse events, we constructed a drug-drug association network, and applied a random walk algorithm to find drug communities with similar adverse event patterns. We developed 14 clusters for comparison with the 14 main groups in the first level of the Anatomical Therapeutic Chemical (ATC) classification system to evaluate relationships between the two classification systems.Results: The retrieved FAERS dataset included 981 drugs and 16,179 adverse events, from which we identified 63,083 significant drug-adverse event pairs. We found new potential safety signals when comparing the drug-adverse event pairs with information from relevant sources. Network analysis of the constructed drug communities revealed connections among drugs, adverse events, and ATC codes, suggesting that drug adverse events may be used to predict the ATC codes of unclassified drugs. For the ATC-classified drugs, the network analysis revealed potential relationships among the drugs by calculating the similarities of the adverse events.Conclusion: The generated drug groups and the drug-drug associations derived from the network analysis might have predictive potential for adverse events, as well as provide information for drug review and development.

Biomedical event extraction has wide applications in biomedicine field. As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Although many approaches have been proposed for biomedical event trigger identification, two main challenges still remain for researchers: 1) most of the existing approaches treat each sentence separately in biomedical documents, failing to make full use of the semantics in the global document context; 2) the sparseness of event triggers leads to a serious issue of imbalanced class for trigger identification. In this paper, we propose an end-to-end framework for biomedical event trigger identification which addresses effectively the two mentioned challenges accordingly. Specifically, a hierarchical attention mechanism is used to model the global document context, including the semantic relationships both among words in the same sentence and among sentences in the same document. In addition, an adaptive class weight learning method is proposed to treat the class imbalance issue in biomedical event trigger identification. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed framework.

Due to the intermittent and random nature of wind energy, wind power interval prediction (WPIP) is important to weaken the uncertainty and support the planning and scheduling of the power system. To improve the quality of WPIP, a novel fuzzy interval prediction model (FIPM) based on the lower upper bound estimation method is proposed in this paper. In the frame of the FIPM, a novel inter type-2 (IT-2) fuzzy model is designed to construct the lower and upper bounds of the prediction interval (PI), in which an IT-2 fuzzy c-regression algorithm is used to partition the data space and identify the fuzzy model. The gravitational search algorithm is employed to optimize the FIPM by minimizing coverage width-based criterion to reach a tradeoff between the interval width and coverage probability. In order to verify the effectiveness of the proposed method, existing interval prediction approaches are adopted in comparative experiments with 17 datasets extracted from five wind fields. The experimental results show that the proposed IT-2 FIPM achieves significant performance with huge promotion in the quality of the PI compared to the traditional forecasting models.

In order to make use of unlabeled data in hyperspectral images (HSIs), a simple but effective semi-supervised learning method based on convolutional neural network (CNN) is proposed for HSIs classification. First, we define a loss function by integrating a clustering loss function for unlabeled data with softmax loss function for labeled data. Here, the labeled features extracted from CNN are not only used for training classifiers, but also providing anchors to initialize a set of clustering centers via K-means method. Then, all data are used to jointly train deep network for HSIs classification. The experimental results demonstrate that our method can achieve comparative results over the traditional supervised learning method based on CNN. Meanwhile, our method has simple network structure and can be easily trained.

Haze-relevant image features are widely used in haze density perception and transmission estimation for single image dehazing, since hazy images usually have distinct features or characters from clean images. However, little attention has been paid to identify haze-relevant features, and further select some compact but informative image features for image dehazing. In this paper, we propose a novel joint feature selection and transmission estimation model named JFSTE via Deep Belief Network (DBN) for image dehazing. First, we develop a one-to-one linear feature selection layer in the DBN, in which each input feature only connects to one node in next layer with a binary weight. Meanwhile, a simple feature-selection strategy is proposed to determine these binary weights by considering the importance of features and the expected selected feature numbers. Second, in order to identify haze-relevant features of hazy image and narrow down their relevance, the minimum redundancy maximum relevancy is introduced into the joint optimization of our network. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method.

Hyper-plane-shaped clustering (HPSC) has been demonstrated to be more effective in Takagi-Sugeno (T-S) fuzzy model identification compared to hyper-sphere-shaped clustering. Although some HPSC algorithms, based on type-2 fuzzy theory, have already been developed and have been demonstrated to have outstanding performance in T-S fuzzy modeling, mismatching of the traditional hyper-sphere-shaped membership function and HPSC results will inevitably restrict the modeling performance. In this paper, a modified inter type-2 fuzzy c-regression model (IT2-FCRM) clustering and new hyper-plane-shaped Gaussian membership function were proposed for T-S fuzzy modeling. In the proposed approach, the coefficients of the upper and lower hyperplanes were deduced based on an IT2-FCRM algorithm. Then, a hyper-plane-shaped membership function was directly defined using the hyperplanes to identify the antecedent parameters of the T-S fuzzy model. The experimental results of several benchmark problems show that identification of T-S model accuracy was greatly promoted.

BACKGROUND: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. METHODS: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. RESULTS: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. CONCLUSION: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

After publication of the original article [1] it was brought to our attention that the following was incorrectly placed under subheading '3.Classification analysis and comparison' of subsection 'Evaluation of topic modeling performance' of the 'Methods' section:Topic model-derived clustering method [33] was applied, in which LDA was utilized as a feature reduction approach for cluster analysis.The LDAderived topics were considered as the new features of datasets.The sampletopic matrix (Fig. 1(f)) was treated as a new representation of the original dataset.Based on the sample-topic matrix (topic number was chosen as 5 and 30, respectively), conventional clustering algorithms, such as k-means, was used for the clustering analysis.The number of clusters was set as 7 in the k-means method due to 7 different serotypes in the dataset.While in comparison, k-means algorithm was also applied on VSM matrix using Hamming Distance similarities.For further comparison, due to the dimension reduction of topic modeling approach, the traditional tool of PCA was used to reduce features (Numbers of 2, 5, 10 and 30 were randomly selected as the reduced features, respectively) of VSM matrix followed by the k-means cluster analysis.Moreover, clustering by only LDA referred as "highest probable topic assignment" [33] (5 and 30 topics were used) was also used for comparison.In "highest probable topic assignment", the LDA-derived topics were made as the clusters of the dataset.Then, each sample was assigned to the cluster (Topic) with the highest probability in the row of the sample-topic matrix.To interpret the clustering results obtained by the k-means algorithm, samples in each cluster were labeled as the dominant serotype of the samples in the cluster.

BACKGROUND: Topic modelling is an active research field in machine learning. While mainly used to build models from unstructured textual data, it offers an effective means of data mining where samples represent documents, and different biological endpoints or omics data represent words. Latent Dirichlet Allocation (LDA) is the most commonly used topic modelling method across a wide number of technical fields. However, model development can be arduous and tedious, and requires burdensome and systematic sensitivity studies in order to find the best set of model parameters. Often, time-consuming subjective evaluations are needed to compare models. Currently, research has yielded no easy way to choose the proper number of topics in a model beyond a major iterative approach. METHODS AND RESULTS: Based on analysis of variation of statistical perplexity during topic modelling, a heuristic approach is proposed in this study to estimate the most appropriate number of topics. Specifically, the rate of perplexity change (RPC) as a function of numbers of topics is proposed as a suitable selector. We test the stability and effectiveness of the proposed method for three markedly different types of grounded-truth datasets: Salmonella next generation sequencing, pharmacological side effects, and textual abstracts on computational biology and bioinformatics (TCBB) from PubMed. CONCLUSION: The proposed RPC-based method is demonstrated to choose the best number of topics in three numerical experiments of widely different data types, and for databases of very different sizes. The work required was markedly less arduous than if full systematic sensitivity studies had been carried out with number of topics as a parameter. We understand that additional investigation is needed to substantiate the method's theoretical basis, and to establish its generalizability in terms of dataset characteristics.

BACKGROUND: The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. RESULTS: In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. CONCLUSION: Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

BACKGROUND: Pulsed field gel electrophoresis (PFGE) is currently the most widely and routinely used method by the Centers for Disease Control and Prevention (CDC) and state health labs in the United States for Salmonella surveillance and outbreak tracking. Major drawbacks of commercially available PFGE analysis programs have been their difficulty in dealing with large datasets and the limited availability of analysis tools. There exists a need to develop new analytical tools for PFGE data mining in order to make full use of valuable data in large surveillance databases. RESULTS: In this study, a software package was developed consisting of five types of bioinformatics approaches exploring and implementing for the analysis and visualization of PFGE fingerprinting. The approaches include PFGE band standardization, Salmonella serotype prediction, hierarchical cluster analysis, distance matrix analysis and two-way hierarchical cluster analysis. PFGE band standardization makes it possible for cross-group large dataset analysis. The Salmonella serotype prediction approach allows users to predict serotypes of Salmonella isolates based on their PFGE patterns. The hierarchical cluster analysis approach could be used to clarify subtypes and phylogenetic relationships among groups of PFGE patterns. The distance matrix and two-way hierarchical cluster analysis tools allow users to directly visualize the similarities/dissimilarities of any two individual patterns and the inter- and intra-serotype relationships of two or more serotypes, and provide a summary of the overall relationships between user-selected serotypes as well as the distinguishable band markers of these serotypes. The functionalities of these tools were illustrated on PFGE fingerprinting data from PulseNet of CDC. CONCLUSIONS: The bioinformatics approaches included in the software package developed in this study were integrated with the PFGE database to enhance the data mining of PFGE fingerprints. Fast and accurate prediction makes it possible to elucidate Salmonella serotype information before conventional serological methods are pursued. The development of bioinformatics tools to distinguish the PFGE markers and serotype specific patterns will enhance PFGE data retrieval, interpretation and serotype identification and will likely accelerate source tracking to identify the Salmonella isolates implicated in foodborne diseases.

Radio Frequency Identification (RFID) technology that is used in pervasive computing environments is widely concerned in recent years. At present, RFID technology is mainly applied in logistics, manufacturing, public service industry and so on. Based on the characteristics of pervasive computing environment, in this paper, a RFID context-aware service model is proposed. The model combines Ontology Language OWL with CC/PP and FOAF specifications to build a context model. Context ontology includes domain ontology and RFID tags ontology. On this basis, the context reasoning and interpretation mechanism based on ontology and rule is introduced. Through the rewritable RFID tag memory, dynamic management of the context in RFID system is realized. Finally, examples are used to demonstrate the feasibility of the model.

BACKGROUND: Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack™, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack™ has been widely adopted by the research community and used for pharmacogenomics data review in the FDA's Voluntary Genomics Data Submission program. RESULTS: ArrayTrack™ has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrack™'s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA). CONCLUSION: The application of ArrayTrack™ to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrack™ is free to use and available to public, private, and academic researchers at http://www.fda.gov/ArrayTrack.