Functional and genetic analysis of the colon cancer network

Emmert‐Streib, Frank; Simoes, Ricardo de Matos; Glazko, Galina V.; McDade, Simon S.; Haibe‐Kains, Benjamin; Holzinger, Andreas; Dehmer, Matthias; Campbell, Frederick Charles

doi:10.1186/1471-2105-15-s6-s6

Cited by 36 publications

(21 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possibility that acquired behavior or marks can be passed from parents to children is in serious debate and the advent of epigenetics is hailed as a profound shift in our understanding of inheritance, i.e. that genes also have a kind of "memory" [23], [24], epigenetics being an upcoming hype in medical research [25], with a very recent example in cancer research found here [26].…”

Section: Lamarck's Theorymentioning

confidence: 97%

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Holzinger

Palade

Rabadán

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

Abstract. Evolutionary Algorithms (EAs) are a fascinating branch of computational intelligence with much potential for use in many application areas. The fundamental principle of EAs is to use ideas inspired by the biological mechanisms observed in nature, such as selection and genetic changes, to find the best solution for a given optimization problem. Generally, EAs use iterative processes, by growing a population of solutions selected in a guided random search and using parallel processing, in order to achieve a desired result. Such population based approaches, for example particle swarm and ant colony optimization (inspired from biology), are among the most popular metaheuristic methods being used in machine learning, along with others such as the simulated annealing (inspired from thermodynamics). In this paper, we provide a short survey on the state-of-the-art of EAs, beginning with some background on the theory of evolution and contrasting the original ideas of Darwin and Lamarck; we then continue with a discussion on the analogy between biological and computational sciences, and briefly describe some fundamentals of EAs, including the Genetic

show abstract

Section: Lamarck's Theorymentioning

confidence: 97%

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Holzinger

Palade

Rabadán

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The basic idea of BC3Net is a bagging version of C3Net [28] which gives conservative estimates of the relations between the variables using estimates of mutual information values. Previously, this method has been successfully used in genomics to infer causal gene regulatory networks from high-dimensional data [29,30] and in finance for inferring investor trading networks [31].…”

Section: Global Community Landscapementioning

confidence: 99%

Defining Data Science by a Data-Driven Quantification of the Community

Emmert‐Streib

Dehmer

2018

MAKE

Self Cite

View full text Add to dashboard Cite

Data science is a new academic field that has received much attention in recent years. One reason for this is that our increasingly digitalized society generates more and more data in all areas of our lives and science and we are desperately seeking for solutions to deal with this problem. In this paper, we investigate the academic roots of data science. We are using data of scientists and their citations from Google Scholar, who have an interest in data science, to perform a quantitative analysis of the data science community. Furthermore, for decomposing the data science community into its major defining factors corresponding to the most important research fields, we introduce a statistical regression model that is fully automatic and robust with respect to a subsampling of the data. This statistical model allows us to define the ‘importance’ of a field as its predictive abilities. Overall, our method provides an objective answer to the question ‘What is data science?’.

show abstract

“…Clustering techniques have been applied to medical problems for some time and there are many different algorithms available, all with very different performances and use cases [26], [27], [28], [29].…”

Section: Cluster Analysis By Dbscan Algorithmmentioning

confidence: 99%

Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System

Yıldırım

Bloice

Holzinger

2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper, a research study to discover hidden knowledge in the reports of the public release of the Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS) for erythromycin is presented. Erythromycin is an antibiotic used to treat certain infections caused by bacteria. Bacterial infections can cause significant morbidity, mortality, and the costs of treatment are known to be detrimental to health institutions around the world. Since erythromycin is of great interest in medical research, the relationships between patient demographics, adverse event outcomes, and the adverse events of this drug were analyzed. The FDA's FAERS database was used to create a dataset for cluster analysis in order to gain some statistical insights. The reports contained within the dataset consist of 3792 (44.1%) female and 4798 (55.8%) male patients. The mean age of each patient is 41.759. The most frequent adverse event reported is oligohtdramnios and the most frequent adverse event outcome is OT(Other). Cluster analysis was used for the analysis of the dataset using the DBSCAN algorithm, and according to the results, a number of clusters and associations were obtained, which are reported here. It is believed medical researchers and pharmaceutical companies can utilize these results and test these relationships within their clinical studies.

show abstract

Functional and genetic analysis of the colon cancer network

Cited by 36 publications

References 59 publications

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Defining Data Science by a Data-Driven Quantification of the Community

Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System

Contact Info

Product

Resources

About