Mei Jing Liew

Bio: Mei Jing Liew is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Support vector machine & Flux balance analysis. The author has an hindex of 2, co-authored 3 publications receiving 59 citations.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Abstract: Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

Software for detecting gene-gene interactions in genome wide association studies

Abstract: Nowadays, genome-wide association studies (GWAS) have offered hundreds of thousands of single nucleotide polymorphism (SNPs). The studies of epistatic interactions of SNPs (denoted as gene-gene interactions or epitasis) are particularly important to unravel the genetic basis to complex multifactorial diseases. However, the greatest challenging and unsolved issue in GWAS is to discover epistatic interactions among large amount of SNPs data. Besides, traditional statistical approaches cannot solve such epistasis phenomenon due to possessing high dimensional data and the occurring of multiple polymorphisms. Hence, various kinds of promising software have been extensively investigated in order to solve these problems. This paper gives an overview on the software that had been used to detect gene-gene interactions that bring the effect on common and multifactorial diseases. Furthermore, sources, link, and functions description to the software are provided in this paper as well. Lastly, this paper presents the language implemented, system requirements, strengths, and weaknesses of software that had been widely used in detecting epistatic interactions in complex human diseases.

In silico gene deletion of escherichia coli for optimal ethanol production using a hybrid algorithm of particle swarm optimization and flux balance analysis

Abstract: Metabolic engineering of microorganism is widely used to enhance the production of metabolites that is useful in food additives, pharmaceutical, supplements, cosmetics, and polymer materials. One of the approaches for enhancing the biomass production is to utilize gene deletion strategies. Flux Balance Analysis is introduced to delete the gene that eventually leads the overproduction of the biomass and then to increase the biomass production. However, the result of biomass production obtained does not achieve the optimal production. Therefore, we proposed a hybrid algorithm of Particle Swarm Optimization and Flux Balance Analysis to attain an optimal gene deletion that is able to produce a higher biomass production. In this research, Particle Swarm Optimization is introduced as an optimization algorithm to obtain optimal gene deletions while Flux Balance Analysis is used to evaluate the fitness (biomass production or growth rate) of gene deletions. By performing an experiment on Escherichia coli, the results indicate that the proposed hybrid algorithm of Particle Swarm Optimization and Flux Balance Analysis is able to obtain optimal gene deletions that can produce the highest ethanol production. A hybrid algorithm is suggested due to its ability in seeking a higher ethanol production and growth rate than OptReg methods.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.

Abstract: In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

Abstract: In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

A survey about methods dedicated to epistasis detection.

Abstract: During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

Alternative techniques for forecasting mineral commodity prices

Abstract: Forecasting mineral commodity (MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory (CT) and machine learning (ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system’s dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system’s behavior and not for forecasting. Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system’s dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality. Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

Pathway and network approaches for identification of cancer signature markers from omics data

Abstract: The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.