การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity Results Here, we present Decagon, an approach for modeling polypharmacy side effects The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type Decagon is developed specifically to handle such multimodal graphs with a large number of edge types Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69% We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies Availability and implementation Source code and preprocessed datasets are at: http://snapstanfordedu/decagon

Modeling polypharmacy side effects with graph convolutional networks.

/pdf/citizen-science-a-study-of-people-expertise-and-sustainable-3po1jwi9qc.pdf

Citizen Science: A Study of People, Expertise and Sustainable Development

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

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Automating drug discovery

Research into dementia prevention is of paramount importance if the dementia epidemic is to be halted. Observational studies have identified several potentially modifiable risk factors for dementia, including hypertension, dyslipidaemia and obesity at midlife, diabetes mellitus, smoking, physical inactivity, depression and low levels of education. Randomized clinical trials are needed that investigate whether interventions targeting these risk factors can reduce the risk of cognitive decline and dementia in elderly adults, but such trials are methodologically challenging. To date, most preventive interventions have been tested in small groups, have focused on a single lifestyle factor and have yielded negative or modest results. Given the multifactorial aetiology of dementia and late-onset Alzheimer disease, multidomain interventions that target several risk factors and mechanisms simultaneously might be necessary for an optimal preventive effect. In the past few years, three large multidomain trials (FINGER, MAPT and PreDIVA) have been completed. The FINGER trial showed that a multidomain lifestyle intervention can benefit cognition in elderly people with an elevated risk of dementia. The primary results from the other trials did not show a statistically significant benefit of preventive interventions, but additional analyses among participants at risk of dementia showed beneficial effects of intervention. Overall, results from these three trials suggest that targeting of preventive interventions to at-risk individuals is an effective strategy. This Review discusses the current knowledge of lifestyle-related risk factors and results from novel trials aiming to prevent cognitive decline and dementia. Global initiatives are presented, including the World Wide FINGERS network, which aims to harmonize studies on dementia prevention, generate high-quality scientific evidence and promote its implementation.

Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease

Motivation A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. Availability and implementation The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . Contact nazong@ucsd.edu. Supplementary information Supplementary data are available at Bioinformatics online.

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Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Importance The COVID-19 pandemic is changing how health care providers practice. As some telemedicine and telecommunication support tools have been incorporated into the otolaryngology practice in response to safety and access demands, it is essential to review how these tools and services can help facilitate facial trauma evaluation during a time when clinical resources are limited. Objective To review applications of telemedicine for the evaluation of facial trauma to better direct utilization of these methods and technologies during times of limited access to clinical resources such as the COVID-19 pandemic. Methods A systematic review was conducted using PubMed, Embase, and Web of Science. Results After screening 158 titles and abstracts, we identified 16 eligible studies involving facial trauma evaluation using telemedicine. Telemedicine opportunities for facial trauma evaluation have the potential to be developed in the areas of multidisciplinary remote consultations, facial trauma triage, patient engagement, and postoperative follow-up. Conclusion The COVID-19 pandemic is posing obstacles for both providers and patients in the delivery of health care at a time of limited clinical resources. Telemedicine may provide a potential useful tool in the evaluation and triage of facial injuries and patient engagement.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/lio2.428

Navigating telemedicine for facial trauma during the COVID-19 pandemic.

Structuralizing biomedical abstracts with discriminative linguistic features

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in predicting drug-target association or the interaction between drugs and targets based on various features the drugs and targets have. Heterogeneous network topology involving many biomedical entities interactions has yet to be used in drug-target association. Deep learning can disclose features of vertices in a large network, which can be incorporated with heterogeneous network topology in order to assist similarity-based solutions to provide more flexibility for drug-target prediction. Here we describe a similarity-based drug-target prediction method that utilizes a topology-based similarity measure and two inference methods based on the similarities. We used DeepWalk, a deep learning method, to calculate the vertex similarities based on Linked Tripartite Network (LTN), which is a heterogeneous network created from different biomedical-linked datasets. The similarities are further used to feed to the inference methods, drug-based similarity inference (DBSI) and target-based similarity inference (TBSI), to obtain the predicted drug-target associations. Our previous experiments have shown that by utilizing deep learning and heterogeneous network topology, the proposed method can provide more promising results than current topology-based similarity computation methods.

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

Abstract Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

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Victoria Ngo

Papers

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Navigating telemedicine for facial trauma during the COVID-19 pandemic.

Structuralizing biomedical abstracts with discriminative linguistic features

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

BETA: a comprehensive benchmark for computational drug–target prediction