Fractal Analysis of Time-Series Data Sets: Methods and Challenges

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

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The Relationship between China’s Population Structure Change in Minority Areas, Economic Growth and Demographic Dividend—Based on an Empirical Analysis of Time Series Data from 1992 to 2012

Open Journal of Business and Management ◽

10.4236/ojbm.2016.42028 ◽

2016 ◽

Vol 04 (02) ◽

pp. 266-272 ◽

Cited By ~ 1

Author(s):

Shanshan Gao ◽

Junlin Shao

Keyword(s):

Economic Growth ◽

Time Series ◽

Population Structure ◽

Empirical Analysis ◽

Time Series Data ◽

Structure Change ◽

Series Data ◽

Demographic Dividend ◽

The Relationship ◽

Analysis Of Time Series

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An Efficient Method for Forecasting Using Fuzzy Time Series

Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-0914-1.ch013 ◽

2017 ◽

pp. 287-304 ◽

Cited By ~ 3

Author(s):

Pritpal Singh

Keyword(s):

Time Series ◽

Time Series Data ◽

Weather Forecasting ◽

Small Error ◽

Fuzzy Time Series ◽

Series Data ◽

Data Sets ◽

Proposed Model ◽

Temperature Forecasting ◽

The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

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