Goutam Saha

Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

An Approach towards Automated Disease Diagnosis & Drug Design Using Hybrid Rough-Decision Tree from Microarray Dataset

Biological databases related to medical science, containing pathological, radiological and genetic information of patients is undergoing tremendous growth, beyond our analyzing capability. However such analysis can reveal new findings about the cause and subsequent treatment of any disease. Here the genetic information of Lung Adenocarcinoma, in the form of microarray dataset has been investigated which have five different stages. Rough Set Theory (RST) has been used in analysis with an aim to effectively extract biologically relevant information, as RST is a tool that works well in an environment, heavy with inconsistent and ambiguous data, or with missing data and provides efficient algorithms for finding hidden patterns in data. The investigation has been carried out on the publicly available microarray dataset obtained from the GEO profiles at National Centre for Biotechnology Information (NCBI) website. Cross validation of the generated rule sets shows 100% accuracy. Now to extract the hidden biological dependencies between responsible genes, Decision Tree is used at consecutive two stages of cancer development to identify the main culprit genes for cancer development from one stage to another and that may lead to the drug design. The analysis revealed that hybrid Rough- Decision Tree is able to extract hidden relationships among the various genes which play an important role in causing the disease and also able to provide a unique rule set for automated medical diagnosis. Moreover at the end, the functions of the identified genes are studied and validated from Gene Ontology website DAVID which clearly shows the direct or indirect relation of genes with the cancer. This study highlights the usefulness and efficiency of RST and Decision Tree in the disease diagnosis process and its potential use in inductive learning and as a valuable aid for building more biologically significant expert systems in medical sciences

Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. In this paper, Bat Algorithm (BA) was applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN). Initially the proposed method is tested against small artificial network without any noise and the efficiency was observed in term of number of iteration, number of population and BA optimization parameters. The model was also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset. In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. Coli. The results shown that it can able to identify the maximum true positive regulation but also include some false positive regulations. Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model

S-system based gene regulatory network reconstruction using Firefly algorithm

The correct inference of gene regulatory network plays a critical role in understanding biological regulation in cells and genome based therapeutics. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Since S-system is based on the rate law, it is considered as a suitable mathematical model for representing complex biological reactions between genes. As, this problem has multiple solutions, optimized solution need to be identified via different nature inspired metaheuristic algorithms. So, in this paper, a new method is elaborated that helps to infer gene regulatory network for Lung Adenocarcinoma using S-system and Firefly Optimization which is an efficient but simple metaheuristic inspired by natural motion of fireflies. By optimizing the values of parameters of the S-system model, gene network can be easily reconstructed and inferred. Though direct biological validation of the network is not possible, but accuracy of the proposed method can be described as how well the network fit with the initial training data for different genes which is quite satisfactory for our research work.

Neural network based gene regulatory network reconstruction

Gene Regulatory Networks (GRN) is used to model the regulations in living organisms. Inferring genetic network from different experimental high throughput biological data (like microarray) is a challenging job for all researchers. In this paper, Artificial Neural Network, which is a very effective soft computing tool to learn and model the dynamics or dependencies between genes, is used for reconstruction of small scale GRN from the reduced microarray dataset of Lung Adenocarcinoma. The significances of regulations of one gene to other genes of the system are expressed by a weight matrix which is computed using Perceptron based biologically significant weight updating method by minimizing the error during learning. Based on the values of elements of filtered weight matrix, a directed weighted graph can be drawn successfully that denotes gene regulatory network.

Identification of Genetic Pathway for Cervical Cancer Development Using Rough and Bayesian Theory

Suitable analysis of microarray dataset can unlock the mystery of the origin of many dreaded disease like cancer which can subsequently be investigated for its rectification, resulting into search for drug design. A critical challenge of the post-genomic era is to find out the cancer causing genes that induce changes in gene expression profiles in the microarray dataset. Various algorithms based on SVM, Data Mining Techniques, Information theory based investigations, Clustering Techniques etc. were used by previous researchers. In this paper, Rough Set Theory and Bayesian Network based techniques have been applied for the same purpose. Rough Set has been used to isolate genes from microarray dataset responsible for cervical cancer. Bayesian approach has been used for extracting the Gene Regulating Network using the isolated genes. The same has been repeated for a normal healthy person. By superimposing these two networks, it is possible to find out the distinct cellular pathway for development of cancer from the departure of directed edges of the two networks. The results obtained in this work are quite satisfactory.

Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid

Current progress in cellular biology and bioinformatics allow researchers to get a distinct picture of the complex biochemical processes those occur within a cell of the human body and remain as the cause for many diseases. Therefore, this technology opened up a new door to the researchers of computer science as well as to biologists to work together to investigate the causes of a disease. One of the greatest challenges of the post-genomic era is the investigation and inference of the regulatory interactions or dependencies between genes from the microarray data. Here, a new methodology has been devised for investigating the genetic interactions among genes from temporal gene expression data by combining the features of Neural Network and Cuckoo Search optimization. The developed technique has been applied on the real-world microarray dataset of Lung Adenocarcinoma for detection of genes which may be directly responsible for the cause of Lung Adenocarcinoma.

Gene regulatory networks using bat algorithm inspired particle swarm optimization

Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has been used for optimising the parameters of the recurrent neural network model. Preliminary research with the proposed methodology has been done on a small, artificial network and the experimental (in vivo) microarray data of the SOS DNA repair network of Escherichia coli. Results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.