Pattern Structures for Analyzing Complex Data

2009 ◽  
pp. 33-44 ◽  
Author(s):  
Sergei O. Kuznetsov
Keyword(s):  
Complex Data ◽  
Pattern Structures

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

Biotransformation of Coumarins by Filamentous Fungi: An Alternative Way for Achievement of Bioactive Analogs

2019 ◽  
Vol 16 (6) ◽  
pp. 568-577 ◽  
Author(s):  
Jainara Santos do Nascimento ◽  
João Carlos Silva Conceição ◽  
Eliane de Oliveira Silva
Keyword(s):  
Filamentous Fungi ◽  
Chemical Reactions ◽  
Biological Activities ◽  
Chemical Transformations ◽  
Chemical Structures ◽  
Pattern Structures ◽  
Pharmacological Interest ◽  
Aspergillus Sp ◽  
The Impact ◽  
Related Compounds

Coumarins are natural 1,2-benzopyrones, present in remarkable amounts as secondary metabolites in edible and medicinal plants. The low yield in the coumarins isolation from natural sources, along with the difficulties faced by the total synthesis, make them attractive for biotechnological studies. The current literature contains several reports on the biotransformation of coumarins by fungi, which can generate chemical analogs with high selectivity, using mild and eco-friendly conditions. Prompted by the enormous pharmacological interest in the coumarin-related compounds, their alimentary and chemical applications, this review covers the biotransformation of coumarins by filamentous fungi. The chemical structures of the analogs were presented and compared with those from the pattern structures. The main chemical reactions catalyzed the insertion of functional groups, and the impact on the biological activities caused by the chemical transformations were discussed. Several chemical reactions can be catalyzed by filamentous fungi in the coumarin scores, mainly lactone ring opening, C3-C4 reduction and hydroxylation. Chunninghamella sp. and Aspergillus sp. are the most common fungi used in these transformations. Concerning the substrates, the biotransformation of pyranocoumarins is a rarer process. Sometimes, the bioactivities were improved by the chemical modifications and coincidences with the mammalian metabolism were pointed out.

Orchestrating Data, Design, and Narrative

2012 ◽  
Vol 2 (2) ◽  
pp. 1-15
Author(s):  
Charles Miller ◽  
Lucas Lecheler ◽  
Bradford Hosack ◽  
Aaron Doering ◽  
Simon Hooper
Keyword(s):  
Online Learning ◽  
Information Visualization ◽  
Essential Element ◽  
Complex Data ◽  
Daily Lives ◽  
Future Design ◽  
Science And Politics ◽  
Interactive Visualizations ◽  
Online Educators ◽  
Data Design

Information visualization involves the visual, and sometimes interactive, presentation and organization of complex data in a clear, compelling representation. Information visualization is an essential element in peoples’ daily lives, especially those in data-driven professions, namely online educators. Although information visualization research and methods are prevalent in the diverse fields of healthcare, statistics, economics, information technology, computer science, and politics, few examples of successful information visualization design or integration exist in online learning. The authors provide a background of information visualization in education, explore a set of potential roles for information visualization in the future design and integration of online learning environments, provide examples of contemporary interactive visualizations in education, and discuss opportunities to move forward with design and research in this emerging area.

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