scholarly journals Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Irene Kouskoumvekaki ◽  
Gianni Panagiotou
Keyword(s):  
Drug Discovery ◽  
Molecular Mechanisms ◽  
Structural Similarity ◽  
Disease Diagnosis ◽  
Data Sets ◽  
Learning Tools ◽  
Metabolomics Data ◽  
Pharmaceutical Molecules ◽  
Close Relatedness

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.

scholarly journals An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

2019 ◽  
Author(s):  
Milla Kibble ◽  
Suleiman A. Khan ◽  
Muhammad Ammad-ud-din ◽  
Sailalitha Bollepalli ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Monozygotic Twin ◽  
R Package ◽  
Data Sets ◽  
Learning Approaches ◽  
Machine Learning Approach ◽  
Using Data ◽  
Genomic Methylation

AbstractWe combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22 – 36, 53% female), where 25 of the twin pairs were substantially weight discordant (delta BMI > 3kg/ m2). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate data sets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called Group Factor Analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

FOSTERING STUDENTS’ EXPERIENCES IN ENGLISH WRITING WITH MICROBLOGGING IN TWITTER

2019 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Pham Thi Thu Hien
Keyword(s):  
Qualitative Research ◽  
Language Learning ◽  
Research Design ◽  
Research Methodology ◽  
Learning Tools ◽  
Structured Interviews ◽  
English Writing ◽  
Qualitative Research Methodology

Twitter and Microblogging are two separate entities but completes each other. Both of them can be used as language learning tools and their potential has been proved by several scholars. This study tries to examine students’ experiences in integrating microblogging with twitter. It is also study about the beneficial roles of microblogging with Twitter in language learning, its relation to writing, and its appropriateness in language learning. This study employs a qualitative research methodology, and case study as its research design. Semi-structured interviews and questionnaires were employed in this study to find out about participants' views about microblogging and Twitter. From this study, it can be concluded that the participants of the study underwent various experiences during the implementation of microblogging with Twitter. They also felt that microblogging with Twitter at some point advantages them to systematically arrange their ideas, and allows them to choose appropriate diction of their ideas. They also stated that Twitter can be an appropriate means in language learning, especially in English writing<em>.</em>

Pure Biologics – From a Bootstrapped Boutique CRO to an Integrated Drug Discovery Public Company

2020 ◽  
Vol 7 (1) ◽  
pp. 48-63
Author(s):  
Sameer Deshmukh ◽  
Przemysław Jurek ◽  
Filip Jelen ◽  
Sabina Tabaczar ◽  
Tomasz Bakowski ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Present Article ◽  
Small And Medium Enterprises ◽  
Public Funding ◽  
Corporate Management ◽  
Publicly Traded ◽  
Biopharmaceutical Company ◽  
Public Company ◽  
Medium Enterprises

The present article is a case study of a Polish biopharmaceutical company, “Pure Biologics”. The company was founded in 2010 by a group of scientists and, over the last nine years, grew substantially from just a few individuals to nearly one hundred professionals. Initially, a privately-funded civil partnership, Pure Biologics, has been transformed into a publicly-traded company. Such a transformation has been possible not only because of the expertise and growing experience of corporate management, but also the specific economic environment and substantial public funding dedicated to innovative Small and Medium Enterprises (SMEs).

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Development of a biomarker database toward performing disease classification and finding disease interrelations

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Shaikh Farhad Hossain ◽  
Ming Huang ◽  
Naoaki Ono ◽  
Aki Morita ◽  
Shigehiko Kanaya ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Molecular Mechanisms ◽  
Clustering Algorithm ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Network Clustering ◽  
Medical Field ◽  
Database Development ◽  
Abnormal State ◽  
Comprehensive Information

Abstract A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL:  http://www.knapsackfamily.com/Biomarker/top.php

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

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