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The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called(More)
AIM In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks(More)
Studies have shown that the brain functions are not localized to isolated areas and connections but rather depend on the intricate network of connections and regions inside the brain. These networks are commonly analyzed using Granger causality (GC) that utilizes the ordinary least squares (OLS) method for its standard implementation. In the past, several(More)
Granger Causality (GC) is an effective tool for determining functional connectivity in time-series data. However, application of GC is limited by the curse of dimensionality in many applications, e.g. Gene Regularity Networks (GRN). Various methods have been proposed to overcome this limitation. To the best of our knowledge, there is no detailed comparative(More)
Temporal information plays a substantial role in accessing Granger Causality. However, new technology limits the availability of data by simultaneously analyzing high dimensional data. Recent studies suggest that this problem can be resolved by reusing the data after reversing the timestamp. Based on this idea, we are proposing a new method called Forward(More)
Discovery of temporal dependence is the basic idea for evaluating gene networks using Granger causality. However, with the advancement of technology, now we can analyze multiple genes simultaneously that result in high dimensional data. Recent studies suggest that more causal information can be retrieved if we reverse the time stamp of time series data(More)
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