Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application

Abstract Background Genotype-phenotype association has been one of the long-standing problems in bioinformatics. Identifying both the marginal and epistatic effects among genetic markers, such as Single Nucleotide Polymorphisms (SNPs), has been extensively integrated in Genome-Wide Association Studies (GWAS) to help derive “causal” genetic risk factors and their interactions, which play critical roles in life and disease systems. Identifying “synergistic” interactions with respect to the outcome of interest can help accurate phenotypic prediction and understand the underlying mechanism of system behavior. Many statistical measures for estimating synergistic interactions have been proposed in the literature for such a purpose. However, except for empirical performance, there is still no theoretical analysis on the power and limitation of these synergistic interaction measures. Results In this paper, it is shown that the existing information-theoretic multivariate synergy depends on a small subset of the interaction parameters in the model, sometimes on only one interaction parameter. In addition, an adjusted version of multivariate synergy is proposed as a new measure to estimate the interactive effects, with experiments conducted over both simulated data sets and a real-world GWAS data set to show the effectiveness. Conclusions We provide rigorous theoretical analysis and empirical evidence on why the information-theoretic multivariate synergy helps with identifying genetic risk factors via synergistic interactions. We further establish the rigorous sample complexity analysis on detecting interactive effects, confirmed by both simulated and real-world data sets.https://deepblue.lib.umich.edu/bitstream/2027.42/142802/1/12864_2018_Article_4552.pd

Designing and sample size calculation in presence of heterogeneity in biological studies involving high-throughput data.

The designing and determination of sample size are important for conducting high-throughput biological experiments such as proteomics experiments and RNA-Seq expression studies, thus leading to better understanding of complex mechanisms underlying various biological processes. The variations in the biological data or technical approaches to data collection lead to heterogeneity for the samples under study. We critically worked on the issues of technical and biological heterogeneity. The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values (MVs) and data heterogeneity. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS. We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We found that the FFPE is better than that of FR for tissue storage. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design. We introduce a web application, PWST (Proteomics Workflow Standardization Tool) to standardize the proteomics workflow. The tool will be helpful in deciding the most suitable choice for each step and studying the variability associated with technical steps as well as the effects of continuous variables. We have used the special cases of general linear model - ANCOVA and ANOVA with fixed effects to study the effects due to various sources of variability. We introduce an interactive tool, “SATP: Statistical Analysis Tool for Proteomics”, for analyzing proteomics expression data that is scalable to large clinical proteomic studies. The user can perform differential expression analysis of proteomics data either at the protein or peptide level using multiple approaches. We have developed statistical approaches for calculating sample size for proteomics experiments under allocation and cost constraints. We have developed R programs and a shiny app “SSCP: Sample Size Calculator for Proteomics Experiment” for computing sample sizes. We have proposed statistical approaches for calculating sample size for RNA-Seq experiments considering allocation and cost. We have developed R programs and shiny apps to calculate sample size for conducting RNA-Seq experiments

Interaction-Based Learning for High-Dimensional Data with Continuous Predictors

High-dimensional data, such as that relating to gene expression in microarray experiments, may contain substantial amount of useful information to be explored. However, the information, relevant variables and their joint interactions are usually diluted by noise due to a large number of non-informative variables. Consequently, variable selection plays a pivotal role for learning in high dimensional problems. Most of the traditional feature selection methods, such as Pearson's correlation between response and predictors, stepwise linear regressions and LASSO are among the popular linear methods. These methods are effective in identifying linear marginal effect but are limited in detecting non-linear or higher order interaction effects. It is well known that epistasis (gene - gene interactions) may play an important role in gene expression where unknown functional forms are difficult to identify. In this thesis, we propose a novel nonparametric measure to first screen and do feature selection based on information from nearest neighborhoods. The method is inspired by Lo and Zheng's earlier work (2002) on detecting interactions for discrete predictors. We apply a backward elimination algorithm based on this measure which leads to the identification of many in influential clusters of variables. Those identified groups of variables can capture both marginal and interactive effects. Second, each identified cluster has the potential to perform predictions and classifications more accurately. We also study procedures how to combine these groups of individual classifiers to form a final predictor. Through simulation and real data analysis, the proposed measure is capable of identifying important variable sets and patterns including higher-order interaction sets. The proposed procedure outperforms existing methods in three different microarray datasets. Moreover, the nonparametric measure is quite flexible and can be easily extended and applied to other areas of high-dimensional data and studies

Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient’s health and the overall function of different organs. In this dissertation, we develop and validate a weighted composite index that aggregates the information from a variety of health biomarkers covering multiple organ systems. The index can be used for predicting all-cause mortality and could also be used as a holistic measure of overall physiological health status. We refer to it as the Health Status Metric (HSM). Validation analysis shows that the HSM is predictive of long-term mortality risk and exhibits a robust association with concurrent chronic conditions, recent hospital utilization, and self-rated health. We develop the HSM using Weighted Quantile Sum (WQS) regression (Gennings et al., 2013; Carrico, 2013), a novel penalized regression technique that imposes nonnegativity and unit-sum constraints on the coefficients used to weight index components. In this dissertation, we develop a number of extensions to the WQS regression technique and apply them to the construction of the HSM. We introduce a new guided approach for the standardization of index components which accounts for potential nonlinear relationships with the outcome of interest. An extended version of the WQS that accommodates interaction effects among index components is also developed and implemented. In addition, we demonstrate that ensemble learning methods borrowed from the field of machine learning can be used to improve the predictive power of the WQS index. Specifically, we show that the use of techniques such as weighted bagging, the random subspace method and stacked generalization in conjunction with the WQS model can produce an index with substantially enhanced predictive accuracy. Finally, practical applications of the HSM are explored. A comparative study is performed to evaluate the feasibility and effectiveness of a number of ‘real-time’ imputation strategies in potential software applications for computing the HSM. In addition, the efficacy of the HSM as a predictor of hospital readmission is assessed in a cohort of emergency department patients

Multi-omic biomarker discovery and network analyses to elucidate the molecular mechanisms of lung cancer premalignancy

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC.2020-01-2

메타분석 전략을 활용한 전사체상 바이오마커의 선별

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2019. 2. 김희발.The Next Generation Sqeuencing (NGS) decade resulted in explosive advancements in technology and on knowledge in the bioinformatic area of science. The timely manner of sequencing together with its cheap prices supported the accumulation of a massive pool of biological data, which lead to new findings. Much more complicated study designs along with the advanced statistical analyses have been proposed, which are responsible for the rise of bioinformatics to one of the fastest growing fields of interdisciplinary science. Inevitably, determining appropriate statistical models and summary methods is directly dependent on the experimental designs. As the results of those studies have to be presented and understood by many specialists in different communities, the summary techniques and presentations are also crucial. Meta analytical approaches on complex study designs can simplify the statistical models and enable appropriate deduction techniques in candidate filtering. The most credible candidates can be detected via multiple testing correction and other guidelines on error pruning. However, suggesting study-specific candidates or understanding the employed models and choosing presentation methods are solely on the analysts discretion so far. In this thesis, the meta-analysis includes 1) multi-population data analysis that analyzes the populations separately (split data analysis), 2) different test methods or statistical models are used for a same dataset, 3) combining and results from an independent study. The major objective is on curating the multiple results into a study-specific biomarker of interest, using meta-analytical approaches. Chapter 2 holds the idea of meta-analysis in a sense that the program itself is made for comparison and summarization of p-values from several test results. The study itself is the first step into the meta-analytical strategies in biomarker selection. It is the most primitive chapter of the thesis, but can be used to compare the meta-analytically defined biomarkers in Chapter 3, for example. A basic set of plots is employed to highlight the most concordant results in different statistical models and tests. The incorporated pairwise scatter plot of the first module simply illustrates the correlation of p-values between a pair of tests or models. In the next module, interactive p-value thresholds are shown in the selected scatter plot, and the results are summarized in a Venn diagram. In the final module, a heatmap-like plot shows comprehensive results of all models/tests used in the study and pinpoints which candidates are concordantly significant in those results. The GUI-program proposed in the chapter is applicable to all studies that generate p-values or other statistics, and is demonstrated under several platforms and designs: microarray, GWAS, RNA-Seq, and family-based study. In Chapter 3, the final candidate genes comprise significant DEGs between male and female cattle in two of the employed pipelines. In the RNA-seq protocol, selection of mRNA relies on the poly-A tails of the reads. Unfortunately, some non-coding RNAs, including the lncRNAs, can be transcribed and have poly-A tails. In this case, transcripts from the lncRNAs are not distinguishable from those of the mRNAs. The chapter elucidates that the inclusion of a lncRNA annotation in the upstream RNA-seq process results in a dramatic difference in significant candidate lists and that the conventional pipeline neglects the quantification of ambiguous gene expression, which may result in erroneous interpretation. The effect of lncRNA annotation is also different among tissues, and such tissue-specific patterns have been attested by the concordance of significance in two different DEG analysis pipelines. In conclusion, we suggest genes that were unaffected by the annotation as most credible, from the original candidates where only the mRNA annotation is used (conventional pipeline). In Chapter 4, a sugar substitute that displays anti-inflammatory/obesity effect is analyzed at a gene-level. A normal diet group (ND), high-fat diet group (HFD), and high-fat diet with D-allulose intake group (ALL) from two tissues, liver and epididymal fat (eWAT), are used for the study. The chapter describes crosstalk genes, which are inter-tissue co-expressed genes that are defined to have concordant regulation pattern between liver and eWAT in this study. The two tissues are chosen for their known interaction. The meta-analytical approach here is to summarize the expression profiles in two different tissues, and to draw the concordantly regulated gene expression between-tissues. Furthermore, the study-specific candidates are the Recovered genes that are initially up- or down-regulated by the high fat diet group, but reverts back to normal-level after D-allulose intake. These genes, selected from the pool of cross-talk genes, showed a correlation with the two inflammation-related genera: Lactobacillus and Coprococcus. For this study, much of the extraneous factors (i.e. exercise, food intake, etc.) are well controlled as it is a mouse study, and such rebound of gene expression can be thought of as the outcome of D-allulose intake. The study employs 3 statistical models for liver and eWAT each, and correlation test to derive the recovered genes through meta-analysis of those models. The final 20 RecGs are concordantly expressed in technical validation by qRT-PCR in both tissues. In displaying the candidates, a modified version of the volcano plot has been proposedthe lava plot, which incorporates p-value, fold-change, and a factor in the statistical model (in this study, the tissue factor has been illustrated). The plot highlights the direction of expression regulation, with fold-change, and the significance of the statistical test with color-coded p-values of two tissues for each point (a gene). For Chapter 5, integration of Trait associated genes and differentially expressed genes requires 4 TAG models and 3 DEG models for each tissues. The study-specific biomarker in this chapter is defined as toggles genes, which are body weight-related in all diet groups, and have specific expression pattern in the high fat diet (HFD) group. Of the genes that have HFD-specific expression pattern, those in direct relation or association with body-weight are a more plausible candidate for obesity. The chapter focuses on the TAGs (based on raw p-value) that are significant DEGs after multiple testing correction. By testing only the significant TAGs in the DEG analysis, I could gain statistical power. Such hierarchical approach is only advantageous when the p-values are adjustedraw p-values from the second analyses will be the same even if more genes are used. By reducing the number of tests in the second step of the hierarchical pipeline, statistical power is gained, and reliable candidates can be detected in larger numbers. From Chapters 2 to 5, various meta-analytical techniques have been suggested and illustrated through NGS datasets. By integrating multiple statistical models and multi-class biomarkers, I have simplified scientific ideas that are specific to the datasets, and derived candidate biomarkers by defining a pipeline to integrate the results. Simple variations in the pipeline and plot characteristics helped to fuse ideas that have not been handled before. Given the results, I anticipate that researchers conducting -omics analyses with or without advanced knowledge in statistics or programming can employ my meta-analytical approaches and plots to efficiently highlight and present their works to a broad spectrum of audiences.차세대 염기서열 분석은 생물정보학을 포함한 생명과학 분야에 기술적으로나 지식적으로 비약적인 발전을 가져왔다. 또한, 차세대 염기서열 분석은 그 신속성과 저렴한 비용으로 인해 수많은 생물학적 데이터의 생산과 이에 관한 연구에 활용되어 왔다. 이는 필연적으로 대용량 자료를 분석할 수 있는 복잡한 통계적 분석 기법의 발전으로 이어졌으며, 생물정보학 이라는 신생 분야의 발전을 촉진하는 원동력이 되었다. 그러나 복잡한 대용량 자료구조 및 통계적 분석 기법은 연구설계나 내용에 대한 직관적인 이해를 방해할 뿐만 아니라, 특히 생물정보학을 도구로서 활용하는 비전공자의 연구에 커다란 걸림돌이 된다. 따라서 메타분석을 사용한 적합한 통계 모형 구축과 바이오마커 선별 같은 생물정보학적 분석파이프라인은 연구자의 연구 내용과 자료를 잘 대변해 줄 수 있어야 한다. 현재, 분석 방법론과 프로그램은 많이 제시되어 있는 상태이지만, 이러한 기술들을 연구자가 실제 연구에 어떻게 효과적으로 적용할 것인가는 자료 특이적이며, 그 분석결과의 해석은 여전히 연구자의 재량에 달려있다. 이 학위논문은 다양한 실험설계 상황에서 각각의 설계에 부합하는 의미 있는 후보 유전자를 발굴해 내기 위한 메타분석기법을 중점을 두고 있다. 2장에서는 생물정보학 분석에서 p값에 대한 메타 분석을 다루고 있다. 특히, 다양한 통계 모형과 검증에서 나온 결과를 비교 및 통합할 수 있는 시각화 방법과 여러 독립된 통계검증 결과에서 동시에 유의한 후보 유전자를 발굴하는 예제를 다루고 있다. 또한 이 장에서 제시된 기법을 사용한 GUI (Graphic User Interface) 기반 프로그램을 microarray, GWAS, RNA-seq, 가족 기반 데이터 등 다양한 형태의 데이터에 적용함으로써, 제시된 프로그램이 p값을 포함한 다양한 통계치에 기반한 연구에 활용될 수 있음을 보였다. 3장에서는 mRNA-seq 데이터 분석에서 long non-coding RNA (lncRNA) 를 고려하지 않음으로써 생기는 분석결과의 문제점과 이에 타격을 입지 않는 바이오마커 선별을 다루고 있다. 일반적으로 mRNA-seq 프로토콜에서 mRNA를 선택적으로 분리해 내는 방법은 poly-A tail을 이용한다. 그러나 lncRNA를 포함한 일부 non-coding RNA 들도 mRNA 와 마찬가지로 전사과정에서 poly-A tail을 가진다. 이러한 경우에 RNA-seq 데이터 내에서 lncRNA 와 mRNA 는 명확히 구분되지 않는다. 이 장에서는 RNA-seq 데이터 분석과정에서 lncRNA annotation 의 고려 유무가 최종 결과인 차등 발현 유전자 결과에 상당한 영향을 미친다는 것을 보여줌으로써, lncRNA를 고려하지 않은 기존의 분석방법이 후보 유전자 발굴에 변수가 될 수 있음을 밝혔다. 더불어, lncRNA annotation이 후보 유전자 결과에 미치는 영향은 조직 별로 다른 양상을 나타낸다는 것을 두 개의 독립적인 차등발현 유전자 분석방법을 통해 보여주었다. 결론적으로 lncRNA annotation 정보의 영향을 받지 않는 유전자들이 mRNA-seq 실험설계목적에 가장 부합되는 후보 유전자 임을 제시하였다. 4장에서는 항 염증과 비만에 효과를 보이는 감미료를 먹인 쥐에 대한 RNA-seq및 Metagenome 분석을 통해 실험 목적에 부합하는 후보유전자발굴 과정을 다루었다. 정상식이집단 (ND), 고지방식이집단 (HFD), D-allulose와 고지방식이집단 (ALL) 에 대해 각각 2개의 조직을 사용하였으며, 고지방식이에 대한 D-allulose의 효과와 밀접하게 관련된 후보유전자를 발굴하기 위하여 Recovery gene (RecG) 을 정의하였다. RecG 은 개념적으로 고지방식이의 유전자 발현 상태에서 D-allulose를 섭취했을 때 정상상태로 돌아가는 유전자를 말하여, 실제 분석에서는 두 조직 모두에서 HFD 집단의 발현이 다른 두 집단에 비해 유의하게 높거나 낮고, ND 와 ALL 집단에서는 발현량이 차이가 없으며, 염증과 관련된 유전자로 정의하였다. 또한 이러한 RecG 의 발현 양상을 효과적으로 보여주기 위하여 기존의 Volcano plot을 변형한 Lava plot 을 고안하였다. Lava plot 은 Volcano plot과 같이 각 유전자에 대한 p-value, fold-change 정보를 보여줌과 동시에, 통계모형에서 추가적으로 고려한 요인(여기서는 조직)에 대한 정보를 보여줄 수 있다. RecG의 염증 관련 미생물과의 관련성을 Metagenome 을 통해 확인하였고, qRT-PCR을 통해 최종 후보 RecG가 두 조직에서 RNA-seq 데이터와 동일한 양상으로 발현하는 것을 확인하였다. 5장에서는 앞서 발굴된 HFD 집단 특이적 유전자들의 발현이 몸무게와 관련이 있는지를 분석하였다. 먼저 Raw p값을 이용하여 형질(몸무게)과 관련된 후보유전자를 발굴하고, adjusted p값을 이용하여 발굴된 후보 유전자에서 고지방식이와 관련된 최종 후보 유전자를 발굴하였다. 이러한 단계적 분석 방법은 실험의 최종 목적이 형질과 연관된 유전자(여기서는 몸무게)인 경우에 1차적으로 후보유전자를 줄여줌으로써 검정력을 높여 줌과 동시에 더 많은 후보유전자를 발굴할 수 있다는 장점이 있다. 결과적으로, 단계적 분석 방법을 통해 몸무게와 고지방식이 모두에 관련이 있는 후보유전자를 발굴하였으며, 그 기능이 염증 또는 종양과 관련이 있는 것을 확인하였다. 이 학위논문에서는 제2장에서부터 5장에 걸쳐 차세대 염기서열 분석 자료에 대한 다양한 메타분석기법을 제시하였다. 구체적으로, 자료에 걸맞는 바이오마커의 선별과 신뢰성 있는 후보유전자를 발굴하기 위한 기법과 더불어, 효과적인 시각화 기법을 통해 과학적 연구결과에 대한 직관적 이해를 도울 수 있는 방안을 제시하였다. 또한 기존 분석 및 시각화 방법에 대한 간단한 변형을 통해 기존에 다뤄지지 않았던 여러 생물학적 주제들을 효과적으로 융합할 수 있었다. 본 논문의 파이프라인들은 여러 분야의 연구자들이 OMICS 분석을 수행할 때 연구결과를 효과적으로 제시하는 데에 도움을 줄 것이라 기대된다.Contents ABSTRACT I CONTENTS VII LIST OF TABLES IX LIST OF FIGURES X CHAPTER 1. LITERATURE REVIEW 1 1.1 NEXT GENERATION SEQUENCING (NGS) 2 1.2 RNA SEQUENCING OR WHOLE TRANSCRIPTOME SHOTGUN SEQUENCING 15 1.3 BIOMARKER SELECTION 23 CHAPTER 2. GRACOMICS: SOFTWARE FOR GRAPHICAL COMPARISON OF MULTIPLE RESULTS WITH OMICS DATA 26 2.1 ABSTRACT 27 2.2 INTRODUCTION 29 2.3 MATERIALS AND METHODS 32 2.4 RESULTS AND DISCUSSION 53 2.5 GRACOMICS INSTRUCTION MANUAL (DOWNLOADED) 63 CHAPTER 3. MULTI-TISSUE OBSERVATION OF THE LONG NON-CODING RNA EFFECTS ON SEXUALLY BIASED GENE EXPRESSION IN CATTLE. 63 3.1 ABSTRACT 64 3.2 INTRODUCTION 66 3.3 MATERIALS AND METHODS 69 3.4 RESULTS AND DISCUSSION 75 CHAPTER 4. DISCOVERING/TRACING THE ANTI-INFLAMMATORY MECHANISM/TRIGGER OF D-ALLULOSE: PROFILE STUDY OF MICORBIOME COMPOSITION AND MRNA EXPRESSION IN DIET-INDUCED OBESE MICE 99 4.1 ABSTRACT 100 4.2 INTRODUCTION 101 4.3 MATERIALS AND METHODS 103 4.4 RESULTS AND DISCUSSION 112 CHAPTER 5. TRACING THE INFLAMMATORY EFFECTS OF HIGH FAT DIET IN OBESITY RELATED TRAITS IN DIET-INDUCED OBESE MICE VIA TRAIT ASSOCIATED GENE DETECTION 139 5.1 ABSTRACT 140 5.2 INTRODUCTION 142 5.3 MATERIALS AND METHODS 144 5.4 RESULTS AND DISCUSSION 150 CHAPTER 6. GENERAL DISCUSSION 162 REFERENCES 167 KOREAN SUMMARY(국문 초록) 185Docto

Microarray Data Mining and Gene Regulatory Network Analysis

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc. By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity