Discovery System Research Articles

Applications of Deep Learning for Drug Discovery Systems with BigData

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

Amino-Acid-Conjugated Natural Compounds: Aims, Designs and Results.

Protein is one of the essential macronutrients required by all living things. The breakdown of protein produces monomers known as amino acids. The concept of conjugating natural compounds with amino acids for therapeutic applications emerged from the fact that amino acids are important building blocks of life and are abundantly available; thus, a greater shift can result in structural modification, since amino acids contain a variety of sidechains. This review discusses the data available on amino acid-natural compound conjugates that were reported with respect to their backgrounds, the synthetic approach and their bioactivity. Several amino acid-natural compound conjugates have shown enhanced pharmacokinetic characteristics, including absorption and distribution properties, reduced toxicity and increased physiological effects. This approach could offer a potentially effective system of drug discovery that can enable the development of pharmacologically active and pharmacokinetically acceptable molecules.

Research use and publishing diversity: The role of organisation research publishing for policy and practice

AbstractDigital technologies have enhanced the capacity for organisations across many sectors to produce, publish, and disseminate research in a variety of formats, and a great deal of it is sought and used in public policy and practice‐related research, yet this diversity is often overlooked in studies of research use. While the need for diverse research sources and formats for public policy and practice is increasingly acknowledged, there have been few studies which articulate and categorise what this diversity looks like in practice, and how research is filtered and selected based on genre, source, and other facets. This article reports on a large‐scale online survey and semi‐structured interviews with research users across multiple sectors in Australia on the materials they access and use for policy and practice work. The results indicate that research users are active information seekers who require online access to diverse genres and formats produced by a range of sources and sectors. However, respondents also faced many barriers to research use, including the cost of subscriptions for academic journals, discoverability of reports and data, poor management of publications by organisations including government, and the time required for filtering and evaluation. Based on these findings I argue that policy research requires a far greater variety of genres and sources than is generally recognised with implications for the way research use and the research publishing system is understood and managed in Australia.Points for practitioners Policy research and implementation requires diverse online sources and resources from multiple sectors, including reports, discussion papers, evaluations, and data, produced by organisations (grey literature), as well as journals and books. However, this paper finds there are major barriers to discovery, filtering, and access to diverse research publications for practitioners, resulting in poor productivity and policy outcomes. To improve the use of evidence for policy and practice, we must invest in efficient discovery, access, and management systems for diverse research publications.

How to implement a decision support for digital health: Insights from design science perspective for action research in tuberculosis detection

Association rule mining has gained much popularity in facilitating disease diagnosis and the healthcare industry's decision-making process. The cases of Drug Resistance Tuberculosis (DR-TB) have substantially increased in recent times and the factors driving its rapid rise have not been clearly identified. This research explores the Frequent Pattern (FP) Growth algorithm to identify recurring relationships, disease co-occurrences, and generate some very interesting diagnostics rules for DR-TB. This is achieved through data extraction from patients' TB health database, transforming raw data into a knowledge discovery system that provides a good idea for further exploration through medical research to clarify the unknown patterns from the obtained result. The FP growth algorithm efficiently generates the frequent symptoms associated with pulmonary tuberculosis and drug-resistance tuberculosis by generating the association rules at minimum confidence of 80%. The analysis performed is knowledgeable and conforms to the clinical practices and design science research processes.

Reference in Discovery: Pilot Study and Future Directions

This poster will describe the results of a pilot study focusing on the reference experiences of librarians working in web-scale discovery systems like Ex Libris Primo and Summon, EBSCO Discovery Service (EDS), and WorldCat Discovery. Web-Scale discovery systems rely on a central index and proprietary algorithms to provide a keyword-based search of library content to users that builds on their existing skills using commercial search engines. These systems are ubiquitous in academic libraries but require a shift in reference practice that has not been fully explored in the literature. This pilot study represents a developing dissertation project that will be in the proposal phase at the time of the poster session. The pilot study was based around four semi-structured qualitative interviews with academic librarians. Though the pilot study revealed some interesting insights on the research questions, one of the lessons learned from this study is that the “one voice” of librarian users is not enough to fully explore this issue. The author will discuss changes to the methodology of the project to include the narratives of users and other stakeholders in the data. The poster will also discuss changes to the theoretical foundations of the study that were developed through the data collected in the pilot study, including a greater focus on information literacy instruction in the reference encounter.

Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

15 (PB001) - Impact of orthotopic versus subcutaneous implantation on patient-derived xenograft transcriptomic profile

Background: Patient-derived xenografts (PDXs) have become the leading model system in oncology drug discovery. Depending on their site of implantation, PDX models from solid tumors can be divided into two categories: subcutaneous (s.c.) and orthotopic (ortho). The advantage of s.c. implantation is that tumor size can be easily monitored, allowing fast validation of drug efficacy. However, since the s.c. microenvironment is likely different from the tissue of origin for most tumor types, there is concern that s.c.

Testing Two Discovery Systems: A Usability Study Comparing Student Perceptions of EDS and Primo

Library discovery systems are a primary tool for research and discovery of local collections in many academic libraries. Usability studies on these systems is an important practice to identify technical issues, determine local customization, and inform continuity in library instruction. Most usability studies, however, are completed on a single discovery system. This study is unique in that it compares EBSCO’s EBSCO Discovery Service (EDS) with Ex Libris’s Primo discovery systems. To date, this is the only published usability study conducted on the two systems. Nineteen undergraduate and graduate participants were led through a series of nine usability tasks in both systems. The quantitative and qualitative results of the study indicate that discovery systems are powerful tools when managed consistently and holistically by addressing the discovery search needs of students and faculty through information literacy instruction and oversight by a discovery working group.

Understanding the Usability of a Literature-Based Discovery System Among Clinical Researchers in Sarawak, Malaysia

The rapid increase in scientific publications makes it difficult for researchers to keep up with the latest literature and to explore new research directions. The literature-based discovery (LBD) systems aim to resolve this issue by bridging literatures from disparate fields to assist researchers in knowledge discovery and the formulation and testing of research hypotheses. Previous studies have focused mainly on evaluating the efficacy of LBD systems by replicating historical LBD events. The usability of LBD systems has been under-researched, which partly explains the low adoption of the systems. This paper presents a survey study that evaluates the usability of a LBD system for knowledge discovery and hypothesis refinement, and also investigates factors affecting its adoption among biomedical researchers in Sarawak, Malaysia. The findings suggest that the adoption of the LBD system is related to their perceived usefulness and perceived difficulty in interacting with the user interface features of the system.

Nature-centered interiority as an urban social-spatial ecology

This essay proposes a conceptual framework to understand how urban, nature-centered interiority can create more social-spatial ecologies to support well-being, access, and delight. The approach expands on established research and strategies to filter design decisions for nature-spatial integration in urban environments that are dynamic, ecologically meaningful, and express human values. The narrative investigates the juxtaposition of natural and artificial, wild and controlled, open versus closed, and interior versus exterior within a Midwest city’s downtown as a case study. By speculating on nature interiority as a spatial system for human discovery and interaction through shared values, the series of illustrations expresses the possibility of augmenting urban interiority with nature to shape dialogue, participation, and access for further integration with culture and place within the surrounding environments.

Plate Number Recognition based on Hybrid Techniques

Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.

Research and Application of the Internet of Things Service Platform Based on Semantic Network

In order to study the Internet of Things service platform based on semantic Network, a service-oriented clustering labeling method is proposed, firstly, a semantic Web service similarity computing method is proposed to solve the shortcomings of the existing methods. This method describes the designed semantic Web service annotation clustering algorithm based on AP algorithm. The distance matrix and related parameters of the algorithm are calculated and assigned according to the results of similarity calculation. The correctness of preference parameter selection will be compared and verified in the service discovery system. The greater the value of damping coefficient, the more iterations, and the slower the convergence rate. However, when the value of damping coefficient is 0.7, the number of iterations is moderate and the convergence rate is the best, which proves that this method has a better effect on service function matching.

The road ahead for applications of mechanics in drug delivery

From vaccination to cancer treatment, controlled drug delivery systems have shown great promise across a wide range of therapies. Understanding the mechanics of advanced drug delivery systems is essential to aid in the design of better treatments for patients. Mechanics has particularly emerged as a key area intersecting engineering and drug delivery. Most drug delivery systems work in direct interaction with the body (e. g. vasculature, GI tract). In this perspective, we focus on three areas highlighting the importance of mechanics for next-generation drug delivery applications. We identify challenges and opportunities for innovation. The areas explored include micro/nano mechanics, gastrointestinal mechanics, and vascular mechanics for next generation drug delivery systems and organoids. First, we review applications of micro/nano technology in fabrication of advanced drug delivery systems. We specifically highlight opportunities for using computational mechanics in drug delivery in micro/nano scales. Second, we include a discussion on mechanical considerations for the design of next-generation cancer therapeutics, drug discovery systems and cell therapy platforms. Finally, we discuss the importance of a detailed understanding of gastrointestinal mechanics in designing orally administered drug delivery systems. Taken together, we highlight areas of mechanics that could be important frontiers for innovation across length scales, disease models and organ systems for advanced therapeutics.

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model’s code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.

Discrete Optimization Model in Constructing Optimal Decision Tree

Decision trees have been well studied and widely used in knowledge discovery and decision support systems. One of the applications of binary integer programming to form decision trees or decision making is the knapsack problem. The knapsack problem is an integer programming problem that involves only one constraint. The knapsack problem is generally illustrated with a bag and an item. The problem to be solved is to maximize the price of goods with a certain capacity that can be loaded by a bag with a certain capacity too. In solving the knapsack problem, it can generally be done directly. In this paper we are interested to show how the implicit enumeration method solves the knapsack problem to form an optimal decision tree

Just-In-Time: the value of online integrated interlibrary loans to broaden access to information resources


 
 
 This research article will discuss a collaboration strategy used between eight postsecondary institutions in Puerto Rico which are part of the library consortia COBIMET. COBIMET is a non-profit organization that works collaboratively with its members to improve virtual library services and information skills in the digital era. The development of collaboration strategies for information sharing through interlibrary loans (ILL) services have become an important service to reduce the gap of information needed by researchers or scholars. These institutions implemented an automated online interlibrary loan system that was integrated to their electronic resources discovery system. The aim of this collaborative agreement was to reduce request time and to provide access to more information resources in a cost-effective manner. The article will present the rationale on implementing an automated ILL Request Process and project experiences.
 
 

The nematode serotonin-gated chloride channel MOD-1: A novel target for anthelmintic therapy

Anthelmintics are used to treat human and veterinary parasitic diseases and to reduce crop and livestock production loss associated with parasitosis. The free-living nematode Caenorhabditis elegans, a model system for anthelmintic drug discovery, has a serotonin (5-HT)-gated chloride channel, MOD-1, which belongs to the Cys-loop receptor family and modulates locomotory and behavioral functions. Since MOD-1 is unique to nematodes, it is emerging as an attractive anthelmintic drug target, but details of MOD-1 function are unclear. Here, we revealed novel aspects of MOD-1 function from the molecular level to the organism level and identified compounds targeting this receptor, which may provide new directions for anthelmintic drug discovery. We used whole-cell current recordings from heterologously expressed MOD-1 to show that tryptamine (Tryp), a weak partial agonist of vertebrate serotonin type 3 (5-HT3) receptors, efficaciously activates MOD-1. A screen for modulators revealed that GABAergic ligands piperazine (PZE) and muscimol reduce 5-HT-elicited currents, thus identifying novel MOD-1 allosteric inhibitors. Next, we performed locomotor activity assays, and we found 5-HT and Tryp rapidly decrease worm motility, which is reversible only at low 5-HT concentrations. Mutants lacking MOD-1 are partially resistant to both drugs, demonstrating its role in locomotion. Acting as an antagonist of MOD-1, we showed PZE reduces the locomotor effects of exogenous 5-HT. Therefore, Tryp- and PZE-derived compounds, acting at MOD-1 through different molecular mechanisms, emerge as promising anthelmintic agents. This study enhances our knowledge of the function and drug selectivity of Cys-loop receptors and postulates MOD-1 as a potential target for anthelmintic therapy.

Correction to: E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

Correction to: E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

Hepatocellular carcinoma chemoprevention by targeting the angiotensin-converting enzyme and EGFR transactivation.

Hepatocellular carcinoma (HCC) is a leading cause of death among cirrhotic patients, for which chemopreventive strategies are lacking. Recently, we developed a simple human cell-based system modeling a clinical prognostic liver signature (PLS) predicting liver disease progression and HCC risk. In a previous study, we applied our cell-based system for drug discovery and identified captopril, an approved angiotensin converting enzyme (ACE) inhibitor, as a candidate compound for HCC chemoprevention. Here, we explored ACE as a therapeutic target for HCC chemoprevention. Captopril reduced liver fibrosis and effectively prevented liver disease progression toward HCC development in a diethylnitrosamine (DEN) rat cirrhosis model and a diet-based rat model for nonalcoholic steatohepatitis–induced (NASH-induced) hepatocarcinogenesis. RNA-Seq analysis of cirrhotic rat liver tissues uncovered that captopril suppressed the expression of pathways mediating fibrogenesis, inflammation, and carcinogenesis, including epidermal growth factor receptor (EGFR) signaling. Mechanistic data in liver disease models uncovered a cross-activation of the EGFR pathway by angiotensin. Corroborating the clinical translatability of the approach, captopril significantly reversed the HCC high-risk status of the PLS in liver tissues of patients with advanced fibrosis. Captopril effectively prevents fibrotic liver disease progression toward HCC development in preclinical models and is a generic and safe candidate drug for HCC chemoprevention.

Pseudomonas aeruginosa: Recent Advances in Vaccine Development.

Pseudomonas aeruginosa is an important opportunistic human pathogen. Using its arsenal of virulence factors and its intrinsic ability to adapt to new environments, P. aeruginosa causes a range of complicated acute and chronic infections in immunocompromised individuals. Of particular importance are burn wound infections, ventilator-associated pneumonia, and chronic infections in people with cystic fibrosis. Antibiotic resistance has rendered many of these infections challenging to treat and novel therapeutic strategies are limited. Multiple clinical studies using well-characterised virulence factors as vaccine antigens over the last 50 years have fallen short, resulting in no effective vaccination being available for clinical use. Nonetheless, progress has been made in preclinical research, namely, in the realms of antigen discovery, adjuvant use, and novel delivery systems. Herein, we briefly review the scope of P. aeruginosa clinical infections and its major important virulence factors.