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. 2021 Jan 28:7:e363.
doi: 10.7717/peerj-cs.363. eCollection 2021.

MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks

Nisar Wani  1 , Khalid Raza  2
Affiliations

MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks

Nisar Wani et al. PeerJ Comput Sci. .

Abstract

High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.

Keywords: GRN inference; Gene regulatory networks; Network biology; Systems biology; large-scale GRN.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Application architecture of MKL-GRNI (A) Combined kernel (B) Decomposed regulation matrices (C) Parallel distribution and model building (D) Model execution (E) Writing results to shared object.
Figure 2
Figure 2. Genomic data fusion by combining kernel matrices from multiple kernels into a single combined kernel.
Figure 3
Figure 3. Performance metrics for parallel MKL-GRNI algorithm: (A) Speedup, (B) Efficiency, (C) Redundancy, (D) Quality.
See this image and copyright information in PMC

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