Towards an environment for data mining based analysis processes in bioinformatics and personalized medicine

Wegener, Dennis; Rossi, Simona; Buffa, Francesca M.; Delorenzi, Mauro; Rüping, Stefan

doi:10.1007/s13721-013-0022-1

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…In bioinformatics and personalized medicine, Dennis Wegener [4] put forward the p-medicine consortium, which is creating a biomedical platform to facilitate the translation from current practice to a predictive, personalized, preventive, participatory and psycho-cognitive medicine by integrating VPH models, clinical practice, imaging and omics data.…”

Section: Introductionmentioning

confidence: 99%

A medical support treatment model based on data mining

Wang

Zhang

et al. 2015

2015 10th International Conference on Computer Science &Amp; Education (ICCSE)

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Chronic hepatitis B is a stubborn disease afflicting mankind. Interferon alpha treatment is one of available antiviral options of treating chronic hepatitis B at present. However, the expensive treatment costs and side effects greatly hindered its application. It is useful in practice to patients suffering from hepatitis B that giving the analysis before treatment and predicting the possible effect of treatment according to the physiological mechanism and pathological condition of the patients themselves. This research focus on setting a hepatitis B auxiliary treatment model based on data mining technique which exploits the relationship between the treatment results and the characteristic of different patients. The experiment results on real data sets show that our method is effective and practicable.

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Section: Introductionmentioning

confidence: 99%

A medical support treatment model based on data mining

Wang

Zhang

et al. 2015

2015 10th International Conference on Computer Science &Amp; Education (ICCSE)

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“…In the presented work, we explore the development patterns of diabetes online communities and seek to understand the factors that characterize and influence community development in this domain. Network analysis [and community detection (Fortunato 2009)] is one of the most practical ways of facing the challenges of mining (Wegener et al 2013) the growing data for meaningful information.…”

Section: Introductionmentioning

confidence: 99%

Characterizing development patterns of health-care social networks

Chomutare

Årsand

Hartvigsen

2013

Netw Model Anal Health Inform Bioinforma

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The growing amount of data in health-care social media requires innovative new analysis methods, which are elementary to exploration of relationship dynamics, in a bid to understand the new roles social media plays in health care. In this work, we use network analysis to explore the temporal nature of two large diabetes social networks, with a view to enhancing our knowledge of the development of community structures and cohesion factors. We compare our findings with analysis of two other nonhealth-care social networks. Current results reveal how diabetes online communities are very dynamic, suggesting diabetes patients are usually actively engaged for periods of less than a year, typically immediately following diagnosis. Additionally, we observe shrinking of both diameter and density, as well as disassortative mixing. The presented empirical study informs future online intervention strategies for promoting health behavior and lifestyle changes among people with diabetes.

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“…Identification of miRNA using computational approaches is emerging as faster and reliable method as compared to laboratory-based cloning approaches. The scope of computational methods is increasing due to low cost, high accuracy and new mining techniques (Wegener et al 2013 (Boguski et al 1993). dbEST is a division of GenBank that contains sequence data of ESTs from a number of organisms (http://www.ncbi.nlm.nih.gov/ dbEST/).…”

Section: Introductionmentioning

confidence: 99%

Computational identification and functional annotation of miRNAs in medicinal plant Helianthus petiolaris

Sahu

Singh

Yadav

et al. 2013

Netw Model Anal Health Inform Bioinforma

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MicroRNAs (miRNAs) are short (*22 nucleotide), non-coding RNAs that regulate messenger RNA (mRNA) or protein levels either by promoting mRNA degradation or by attenuating protein translation. Computation identification of miRNAs and their targets in medicinal plant Helianthus petiolaris (H. petiolaris) was carried out. Computational approach for mining of miRNA from EST databases is faster and reliable. Here, the total 27,484 EST sequences of H. petiolaris were assembled and clustered into 4,182 contigs (15 % of EST sequences). These contigs were searched against known miRNA sequences of different plants. An overall 117 unique sequences (precursor miRNAs) were mined from UniGenes. Functional annotation of unique sequences was carried out to eliminate the coding sequences and secondary structure of resulting precursor miRNAs (pre-miRNAs) was determined. The analysis led to identification of 11 miRNAs sequences (10 % of pre-miRNAs). The hpe-mir-395 was found to be the most stable miRNA on the basis of free energy of secondary structure. Targets for novel miRNAs were determined by searching homology between identified miRNA and UniGenes. Eleven miRNA targets were identified of which 67 % are responsible for biological function. The predicted miRNAs were validated by phylogenetic study and belong to four large miRNA families (mir-393, mir-156, mir-1863, mir-169).

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Towards an environment for data mining based analysis processes in bioinformatics and personalized medicine

Cited by 8 publications

References 23 publications

A medical support treatment model based on data mining

A medical support treatment model based on data mining

Characterizing development patterns of health-care social networks

Computational identification and functional annotation of miRNAs in medicinal plant Helianthus petiolaris

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