Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

BACKGROUND: Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. RESULTS: We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. CONCLUSIONS: We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

BACKGROUND: Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation. RESULTS: To address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods. CONCLUSIONS: SimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.

MOTIVATION: Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. RESULTS: ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. AVAILABILITY AND IMPLEMENTATION: ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Virtualization has become one of the most helpful techniques, and today it is prevalent in several computing environments including desktops, data-centers, and enterprises. However, an I/O scalability issue in virtualized environments still needs to be addressed because I/O layers are implemented to be oblivious to the I/O behaviors on virtual machines (VM). In particular, when a multi-queue solid state drive (SSD) is used as a secondary storage, each VM reveals semantic gap that degrades the overall performance of the VM by up to 74%. This is due to two key problems. First, the multi-queue SSD accelerates the possibility of lock contentions. Second, even though both the host machine and the multi-queue SSD provide multiple I/O queues for I/O parallelism, existing Virtio-Blk-Data-Plane supports only one I/O queue by an I/O thread for submitting all I/O requests. In this paper, we propose a novel approach, including the design of virtual CPU (vCPU)-dedicated queues and I/O threads, which efficiently distributes the lock contentions and addresses the parallelism issue of Virtio-Blk-Data-Plane in virtualized environments. We design our approach based on the above principle, which allocates a dedicated queue and an I/O thread for each vCPU to reduce the semantic gap. We also implement our approach based on Linux 3.17, and modify both the Virtio-Blk frontend driver of guest OS and the Virtio-Blk backend driver of Quick Emulator (QEMU) 2.1.2. Our experimental results with various I/O traces clearly show that our design improves the I/O operations per second (IOPS) in virtualized environments by up to 167% over existing QEMU.

UNLABELLED: : Quantitative assessment of chemical reaction similarity aids database searches, classification of reactions and identification of candidate enzymes. Most methods evaluate reaction similarity based on chemical transformation patterns. We describe a tool, RxnSim, which computes reaction similarity based on the molecular signatures of participating molecules. The tool is able to compare reactions based on similarities of substrates and products in addition to their transformation. It allows masking of user-defined chemical moieties for weighted similarity computations. AVAILABILITY AND IMPLEMENTATION: RxnSim is implemented in R and is freely available from the Comprehensive R Archive Network, CRAN (http://cran.r-project.org/web/packages/RxnSim/). CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.