Search results

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.

The literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.

The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.

The drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

An important precondition for resource redeployment is that firms are aware of the commercial applications for which their resources can be used. We take an inventing-firm perspective and ask: how many new commercial applications will a firm associate with an existing technological invention? We note that both technological and organizational characteristics determine the number of distinct applications firms consider feasible for a given technological invention. In particular, we suggest that inherently fungible technologies, that is, technologies that have a broad impact on other technological fields (highly general technologies), will be associated with a larger set of commercial applications. We also suggest that linking applications to an inherently general technology can be challenging when the technology is already embedded in organizational (commercial) routines. Proprietary data from an online marketplace allow us to investigate the applications firms consider feasible for their technological inventions. In line with extant work, a firm assigns a greater number of applications to more general technologies. As expected, however, this relationship is shaped by how the technology is embedded within the organization. Our results have implications for redeployment as firms may face challenges in the initial step of redeployment when fungible resources need to be linked to emerging market opportunities.

This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed.

The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted.

It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums.

To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

The purpose of this study is to shed light on the importance of innovation and patient centricity to improve the existing pharmaceutical products. In the pharmaceutical industry, defining the “value” or “added value” of medicines is a very complex issue that requires objective information about how they can make people healthier and what “value” means to patients. In light of “value creation” strategy for the existing medicines, many original and generic pharmaceutical companies hope to create new portfolios to enter into new markets, to capture more market share or to strengthen their market position in the existing markets. The fact that “value” and “added value” are not static and change rather rapidly over the time, cannot always facilitate market access for the so-called improved or repurposed/repositioned versions now claiming “added-value” status. The only way “added value” category can promote smarter drug pricing scenarios and access to markets, is focusing on “patient Centricity” while innovating for satisfying their unmet medical needs.

This article is designed and structured by two methodologies: the first one is based on face to face interviews and the second one is focused on literature review regarding the value added pharmaceuticals.

There is an increasing confusion regarding “value-added” pharmaceuticals. This term is mainly used to define improved generic versions. This article discusses the “added-value” of this improvement for patients and manufacturers. By launching such products, these companies attempt to become more “innovative and patient-centric”. Furthermore, adopting a patient-centric strategy as a framework for optimizing the modified pharmaceuticals can create value, new pricing models may emerge through this strategy and can promote the “value” of these products by facilitating their access to higher margins.

Limited to an European discussion around the value-added pharmaceuticals.

The facts about value added medicines. The real classifications of these products.

For patients and health care systems, it is important to trust to real value in pharmaceutical treatments. If their “added value” is not justified by scientific proofs, if it is not patient centric, then they can occupy the status of the next generation of generic medicines a category between innovative and pure generics.

This is an original topic which was never discussed before.

A huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.

This study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.

This paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.

This study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.

The purpose of this paper is to show that despite welfare retrenchment and political rhetoric towards welfare, spending on residential addiction treatment should be protected.

Examining benefits in context of costs, the research used social return on investment to monetise benefits and compare with costs. Based at a residential addiction centre, the research used questionnaires and focus groups with residents and former residents.

The centre created almost £4 of benefit for every £1 of cost. Whilst the bulk of savings came from health, housing and criminal justice, there was also a regenerative impact for the local economy.

Sampling in sensitive themes is always problematic, however, the research had contact with many respondents, achieved data saturation and used the centre's success rate as a guide to weight the findings.

The benefits of addiction treatment go beyond health outcomes and raise questions about how this should be reflected in cost distribution. Consequently, this has implications for the ways in which addiction services should be measuring their successes beyond solely health outcomes.

Existing research has largely overlooked the benefit of addiction treatment to the local economy and the fact that, as an investment, this benefit will continue to grow as more people enter the labour market over time.

The research recognises the political context of funding and measures success beyond solely health outcomes. Furthermore, the research recognises the regenerative impact of addiction treatment, which is often overlooked in similar research.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.