Discovering Complex Relationships of Drugs over Distributed Knowledgebases

The common access to the broadband Internet allows small and medium enterprises (SME) simpler participation in various supply chains [3]. However the main problem still is lack of consistency between processes which are undertaken by various supply chain participants. The aim of this paper is to propose the concept of broader cooperation between manufacturing companies based on semantic web and multi-agent system. The background of this paper refers to previous experiences of the authors by implementation of the CORELOG project. It promotes cooperation in logistics services among manufacturing companies located in the Bologna metropolitan area in order to increase companies’ competitiveness as well as environmental sustainability through the rationalization of the logistics processes. The application of semantic web and multi-agent system in the above mentioned project allows the efficient automatic data collection about logistics services providers and their resources, as presented in the Internet.

Availability at large-scale semantic web data can be considered as an opportunity to be used in data source analysis. This analysis process might not obtain data as required since the data is extracted from external sources. This incompleteness of data source can affect analysis result. To resolve this problem, this paper proposed a method for generating data from a transformation process that built based on semantic web technology (ontologies). The application will apply an algorithm that can find a superclass and a subclass of superclass ontology, by mapping the instances of relevant object property to the ontology classes. The classes are then added to the source as a data object instances. In the mapping process, the data is represented by using semi structured format RDF (Resource Description Framework). Data instances generated from this mapping process will complete the semantic data sources and then support the analysis process.

The ultimate objective is in healthcare systems is to boost healthcare services. This evolvement of better biomedical products to a great extent relies upon the capacity to share and connect the abundance of gathered clinical information. The major issue concerning this goal is not just empowering the union of the information but to make information examination and interfaces easy to understand. To achieve this, Semantic Web (SW) technologies can be utilized, which provide standards and inter-operable rich semantic metadata for intelligent applications. This paper, overviews and examines different predominant research headings in the areas of SW and healthcare. In particular, we summarize (1) ontologies in healthcare, (2) the usage of SW technologies for representing patient records, (3) discuss healthcare approaches that utilizes SW, (4) discuss interoperability issue because of the heterogeneous healthcare data, (5) investigate integration of IoT and artificial intelligence techniques into smart semantic healthcare systems and (6) outline SW based security methodologies. Finally, we conclude with future research challenges.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

This correspondence paper presents a prototype Semantic Web system called ManuHub (i.e., Manufacturing Hub) that administers distributed manufacturing services provided by ubiquitous virtual enterprises and allows a function of exploration with friendly graphical user interfaces. ManuHub is practical and useful in creating local manufacturing service hubs via ontology- and constraint-based modeling of the distributed manufacturing services and semantic annotation of the acquired manufacturing services. In particular, ManuHub facilitates efficient, accurate, and automatic retrieval of the required manufacturing services derived from the semantic matchmaking of manufacturing service capabilities. In other words, ManuHub functions as the "binding glue" between collaborative enterprises in order to achieve seamless manufacturing interoperability. The prototype system was built and evaluated with an example illustrated from manufacturing industries so as to demonstrate the feasibility of the proposed approach.

Many important advances of deep learning techniques have originated from the efforts of addressing the image classification task on large-scale datasets. However, the construction of clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that can learn an image classification model from noisy web data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. With these challenges in mind, we assume every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags, and the labels. In this manner, the influence of bias and noise in the data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks first place in the image classification task.

Extracting minimal functional dependencies (MFDs) from relational databases is an import database analysis technique. With the advent of big data era, it is challenging to discover MFDs from big data, especially large-scale distributed data stored in many different sites. The key to discovering MFDs as fast as possible is pruning the useless candidate MFDs. And in most existed algorithms, it usually prunes candidate MFDs from top to bottom or from bottom to top. We present a new algorithms FastMFDs for discovering all MFDs from large-scale distributed data both from top to bottom and from bottom to top in parallel. We experimented our algorithm in real-life datasets, and our algorithm is more efficient and faster than the existed discovering algorithms.

Background and Objectives: In drug research and development whereby, the patient is the main focus, the mission is to overcome the disease and improve the quality of life. The process is designed to ensure that the newly produced medicines are SAFE, EFFECTIVE, and available for the patient in a brief period. As per FDA, drug development and discovery involve five main steps: discovery and development, preclinical research, clinical research, FDA review, and FDA post-market safety and monitoring. With several disease outbreaks, drug discovery, and development has been essential tool in saving lives and ensuring the standard health status. Through research, it brings new drugs into the market against targeted diseases. With the aim of D3, the drugs are assessed under surveillance and monitoring to ensure their safety and efficacy. Methods: Several methods have been generated to develop drugs in a brief period. Following the FDA process of drug development and discovery, a technological method is set per each step, these include computerized methods like High-throughput screening and Computer-based design used in designing a drug hit candidate and the use of pharmacological software to study the mechanism of action of the disease as well as the effectiveness and metabolism of the drug. Results: Different studies show that research and development of drugs through drug discovery has been an essential tool in ensuring a better quality of life. It helps us understand the health condition allowing us to design drugs for newly occurring diseases and even previously untreatable conditions. Conclusion: To play a vital role in saving lives and ensuring the best health status, it is important to understand the process of D3. FDA uses the data provided in the process to decide on different complications found in both prescribed and OTC drugs, hence controlling the serious adverse effects. This improves the process functioning as well as raises the quality of research and development.

Background. The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.Description.SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions. SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies.

In recent times size of the data is increasing rapidly. Existing centralized system is not able to handle large dataset beyond a certain limit. So, we need ontology based approach in Semantic Web and distributed system. Ontology describes a formal specification of certain domain. Existing platform and servers are unable to process different types of data like structured, semi-structured and quasi-structured data to have an effective large number of ontologies in the web. We need the Hadoop platform to implement the incremental and distributed ontologies over and above existing ontologies in Semantic Web. RDF is the basic representation of ontologies and it is used to describe the knowledge in the Semantic Web. The challenges is using RDF include structure of RDF and the memory that is needed for the incremental comparison. MapReduce is one of the main Hadoop components are needed during the mapping of ontologies in the Semantic Web using RDF. In this paper better way of using incremental RDF in Semantic Web is discussed.

Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for “zero-shot” generalisation. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model for video understanding tasks, with minimal training. Specifically, we propose to optimise a few random vectors, termed as “continuous prompt vectors”, that convert video-related tasks into the same format as the pre-training objectives. In addition, to bridge the gap between static images and videos, temporal information is encoded with lightweight Transformers stacking on top of frame-wise visual features. Experimentally, we conduct extensive ablation studies to analyse the critical components. On ten public benchmarks of action recognition, action localisation, and text-video retrieval, across closed-set, few-shot, and zero-shot scenarios, we achieve competitive or state-of-the-art performance to existing methods, despite optimising significantly fewer parameters. Due to space limitation, we refer the readers to the arXiv version at https://arxiv.org/abs/2112.04478.

Semantic Web technologies are intertwined with decision-making processes. In this paper the general objectives of the semantic web tools are reviewed and characterised, as well as the categories of decision support tools, in order to establish an intersection of utility and use. We also elaborate on actual and foreseen possibilities for a deeper integration, considering the actual implementation, opportunities and constraints within the context of decision-making. We conclude that a broader or generalised Semantic Web integration in the decision support community is still a work in progress and much remains to be done.

In pharmaceutical research drug discovery and development process is timeconsuming and expensive. In many cases, it produces incompetent results due to the failure of in vitro and in vivo conventional approaches. Before any new drug is placed in the market it must undergo rigorous testing to get FDA approval. Due to the several limitations imposed by the drug discovery process, in recent times in silico approaches are widely applied in this field. The purpose of this review is to highlight the current molecular docking strategies used in drug discovery and to explore various advances in the field. In this review we have compiled database after an extensive literature search on docking studies which has found its applications relevant to the field of formulation and development. The papers retrieved were further screened to appraise the quality of work. In depth strategic analysis was carried out to confirm the credibility of the findings. The papers included in this review highlight the promising role of docking studies to overcome the challenges in formulation and development by emphasizing it's applications to predict drug excipient interactions which in turn assist to increase protein stability; to determine enzyme peptide interactions which maybe further used in drug development studies; to determine the most stable drug inclusion complex; to analyze structure at molecular level that ascertain an increase in solubility, dissolution and in turn the bioavailability of the drug; to design a dosage form that amplify the drug discovery and development process. This review summarizes recent findings of critical role played by molecular docking in the process of drug discovery and development. The application of docking approach will assist to design a dosage form in the most cost effective and time saving manner.

Grid computing technology is able to integrate and share large-scale distributed computation and data resource to facilitate the scientific researches. Recently, the grid workflow support and large-scale distributed data management are becoming two main requirements of scientists and researchers in many fields, such as bioinformatics, high-energy physics etc. In this paper, we proposed to support grid workflow for data intensive applications using CSF4 scheduling plug-ins. The grid workflow scheduling and data aware scheduling policies are implemented in two scheduling plug-ins, grid workflow plug-in and grid data aware plug-in, respectively. The two scheduling plug-ins can work together smoothly. The data aware plug-in will automatically dispatch the workflow tasks to the grid sites which are close to data replicas. At last, the experiment results are given to show the improvement of system performance and optimization of scheduling.

ABSTRACTIntroduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.