Using Affinity Propagation Combined Post-Processing to Cluster Protein Sequences

Yang, Fan; Zhu, Qingxin; Tang, Dongming; Zhao, Mingyuan

doi:10.2174/092986610791190255

Cited by 11 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Affinity propagation is used in the clustering of microarray and gene expression data [154][155][156] and in sequence analysis [157]. Its versatility beyond bioinformatics [158], especially in natural language processing (NLP) [159][160][161] and computer vision [162,163], suggests that this article's mathematically modest clustering exercise can benefit from interdisciplinary cross-pollination.…”

Section: Clustering Through Affinity Propagationmentioning

confidence: 99%

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

Chen

Zovko²,

Šimurina

et al. 2021

IJERPH

View full text Add to dashboard Cite

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM2.5 pollution.

show abstract

Section: Clustering Through Affinity Propagationmentioning

confidence: 99%

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

Chen

Zovko²,

Šimurina

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…It has been proved that APC is a fast clustering algorithm especially in the case of a large number of samples, and has several advantages on speed, general applicability and good performance (Bodenhofer et al , 2011). So far, APC has been used successfully for microarray/gene expression data clustering (Frey and Dueck, 2007), sequence analysis (Yang et al , 2010), text clustering (Guan et al , 2010), data stream clustering (Zhang et al , 2013), vehicular ad hoc networks clustering (Hassanabadi et al , 2014), among others. Especially, for large data sets, APC offers obvious advantages over existing methods (e.g.…”

Section: Testings On a Private Carmentioning

confidence: 99%

Driving performance grading and analytics: learning internal indicators and external factors from multi-source data

Zhou

Zhao

et al. 2021

IMDS

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.Design/methodology/approachThe authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).FindingsThe developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.Practical implicationsThis study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.Originality/valueThis study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.

show abstract

“…As outlined by Bodenhofer et al [81], AP is especially appropriate for bioinformatics purposes because: (i) numerous similarity scales applied in bioinformatics are not associated with explicit vectorial features; and (ii) detecting a small set of clusters can offer the opportunity for exploration in biological datasets. So far, AP algorithm has been demonstrated to be effective for the purpose of for microarray data analysis [80][81][82][83][84][85], Network analysis [86][87][88] structural biology studies [89][90][91], and sequence analysis [92]. For review, see [81].…”

Section: Clusteringmentioning

confidence: 99%

Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

View full text Add to dashboard Cite

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given.

show abstract

Using Affinity Propagation Combined Post-Processing to Cluster Protein Sequences

Cited by 11 publications

References 0 publications

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

Driving performance grading and analytics: learning internal indicators and external factors from multi-source data

Intelligent mining of large-scale bio-data: Bioinformatics applications

Contact Info

Product

Resources

About