Knowledge Graphs (KGs), composed of triples in the form of (head, relation, tail) and consisting of entities and relations, play a key role in information retrieval systems such as question answering, entity search, and recommendation.In real-world KGs, although many entities exist, the relations exhibit a long-tail distribution, which can hinder information retrieval performance.Previous few-shot knowledge graph completion studies focused exclusively on the positive triple information that exists in the graph or, when negative triples were incorporated, used them merely as a signal to indicate incorrect triples.To overcome this limitation, we propose Relation-Based Conditional Diffusion with Attention Pooling (ReCDAP).First, negative triples are generated by randomly replacing the tail entity in the support set.By conditionally incorporating positive information in the KG and non-existent negative information into the diffusion process, the model separately estimates the latent distributions for positive and negative relations.Moreover, including an attention pooler enables the model to leverage the differences between positive and negative cases explicitly.Experiments on two widely used datasets demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance.The code is available at https://github.com/hou27/ReCDAP-FKGC.

The Gene Ontology (GO) database contains approximately 40,000 classes of terms arranged in a hierarchical relationship. These terms mainly define protein functions and are used in bioinformatics to automatically predict protein functions using their sequences. Recently, several models have been studied, such as ProtBert and ProteinBERT, which predict protein functions by fine-tuning a pretrained model of the nucleotide sequence using a self-supervised deep method. We proposed two models to predict GO using protein features extracted by the ProtBert model to annotate proteins with their GO terms. Additionally, we customized the ProteinBERT model and fine-tuned it to predict GO terms. The experiment showed that protein embeddings created using pretrained transformer models can be used as a source of data for tasks involving sequence prediction, with a focus on protein functions. The suggested models allow flexible sequence lengths and provide improved performance compared to other comparison methods.

Gene annotation tools for the identification of gene functions are often based on similarity with reference sequences, such as those of model organisms. If sequence data for a relevant model organism are not available, it is necessary to use data for closely related organisms, but methods for identifying related organisms are computationally intensive. We propose the application of LSTM (long short-term memory) models for the automatic annotation of genes by generating a training model with sequences of same taxonomic group. The proposed method to identify unknown sequences enables annotation without reference sequences.

SUMMARY: In comparative and evolutionary genomics, a detailed comparison of common features between organisms is essential to evaluate genetic distance. However, identifying differences in matched and mismatched genes among multiple genomes is difficult using current comparative genomic approaches due to complicated methodologies or the generation of meager information from obtained results. This study describes a visualized software tool, geneCo (gene Comparison), for comparing genome structure and gene arrangements between various organisms. User data are aligned, gene information is recognized, and genome structures are compared based on user-defined GenBank files. Information regarding inversion, gain, loss, duplication and gene rearrangement among multiple organisms being compared is provided by geneCo, which uses a web-based interface that users can easily access without any need to consider the computational environment. AVAILABILITY AND IMPLEMENTATION: Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/geneCo. The main module of geneCo is implemented by Python and the web-based user interface is built by PHP, HTML and CSS to support all browsers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

System performance plays an important role in the era of big data. As such, the multiplication algorithm and hardware have evolved to improve system performance which can support a fast big data analysis engine. Several variations on conventional Booth multiplication algorithm have improved performance. However, we have observed that the existing multiplication algorithms repeating addition and shift operations conduct unnecessary iteration due to leading zeros in binary numbers. We have found that we can reduce the number of iterations by clearing unnecessary leading zeros in the multiplier. Thus, we propose two approaches to clear unnecessary leading Os: (1) pre-front-based: pre-clearing leading zeros with the front-based multiplication algorithm and (2) post-booth: post-clearing leading zeros with the rear-based multiplication algorithm using Booth algorithm. We evaluate performance with the data extracted from sparse matrices in UF Matrix Collection. Our algorithms outperform conventional multiplication algorithms by using a minimum number of necessary binary digits in the multiplier. The algorithms can be applied directly below the level of multiplication instructions with a minimal change to deal with big data ingestion.

Summary: Next-generation sequencing (NGS) technologies have led to the accumulation of high-throughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals. We have developed a web application AGORA for the fast, user-friendly and improved annotations of organellar genomes. Annotator for Genes of Organelle from the Reference sequence Analysis (AGORA) annotates genes based on a basic local alignment search tool (BLAST)-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence and visualization of gene map by OGDRAW. Availability and implementation: Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/. The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to one species, are not available for high-volume data processing, or require the use of data derived by experiments such as microarray analysis. To meet the increasing need for high throughput, automated annotation of fungal genomes, we have developed a tool for annotating fungal protein sequences with terms from the Gene Ontology. RESULTS: We describe a classifier called PoGO (Prediction of Gene Ontology terms) that uses statistical pattern recognition methods to assign Gene Ontology (GO) terms to proteins from filamentous fungi. PoGO is organized as a meta-classifier in which each evidence source (sequence similarity, protein domains, protein structure and biochemical properties) is used to train independent base-level classifiers. The outputs of the base classifiers are used to train a meta-classifier, which provides the final assignment of GO terms. An independent classifier is trained for each GO term, making the system amenable to updating, without having to re-train the whole system. The resulting system is robust. It provides better accuracy and can assign GO terms to a higher percentage of unannotated protein sequences than other methods that we tested. CONCLUSIONS: Our annotation system overcomes many of the shortcomings that we found in other methods. We also provide a web server where users can submit protein sequences to be annotated.

The Gene Ontology (GO) is a controlled vocabulary of terms to describe protein functions. It also includes a hierarchical description of the relationships among the terms in the form of a directed acyclic graph (DAG). Several systems have been developed that employ pattern recognition to assign gene function, using a variety of features, including sequence similarity, presence of protein functional domains and gene expression patterns, but most of these approaches have not considered the hierarchical structure of the GO. The DAG represents the functional relationships between the GO terms, thus it should be an important component of an automated annotation system. We propose a Bayesian, multi-label classifier that incorporates the relationships among GO terms found in the GO DAG. A comparative analysis of our method to other previously described annotation systems shows that our method provides improved annotation accuracy when the performance of individual GO terms are compared. More importantly, our method enables the classification of significantly more GO terms to more proteins than were previously possible.