27,088 research outputs found
Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine
The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.Peer reviewe
Predicting new molecular targets for rhein using network pharmacology
<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.</p> <p>Methods</p> <p>In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.</p> <p>Results</p> <p>Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.</p> <p>Conclusions</p> <p>Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.</p
A GPU-based multi-criteria optimization algorithm for HDR brachytherapy
Currently in HDR brachytherapy planning, a manual fine-tuning of an objective
function is necessary to obtain case-specific valid plans. This study intends
to facilitate this process by proposing a patient-specific inverse planning
algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization
(gMCO).
Two GPU-based optimization engines including simulated annealing (gSA) and a
quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in
parallel. After evaluating the equivalence and the computation performance of
these two optimization engines, one preferred optimization engine was selected
for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR
cases were divided into validation set (100) and test set (462). In the
validation set, the number of Pareto optimal plans to achieve the best plan
quality was determined for the gMCO algorithm. In the test set, gMCO plans were
compared with the physician-approved clinical plans.
Over 462 cases, the number of clinically valid plans was 428 (92.6%) for
clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with
target V100 coverage greater than 95% was 288 (62.3%) for clinical plans and
414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO
algorithm to generate 1000 Pareto optimal plans.
In conclusion, gL-BFGS is able to compute thousands of SA equivalent
treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast
and robust multi-criteria optimization algorithm was implemented for HDR
prostate brachytherapy. A large-scale comparison against physician approved
clinical plans showed that treatment plan quality could be improved and
planning time could be significantly reduced with the proposed gMCO algorithm.Comment: 18 pages, 7 figure
The re-emergence of natural products for drug discovery in the genomics era
Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
Nano on reflection
A number of experts from different areas of nanotechnology describe how the field has evolved in the last ten years
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