Jack Y. Yang

Synergistically integrating multi-layer genomic data at systems level not only can lead to deeper insights into the molecular mechanisms related to disease initiation and progression, but also can guide pathway-based biomarker and drug target identification. With the advent of high-throughput next-generation sequencing technologies, sequencing both DNA and RNA has generated multi-layer genomic data that can provide DNA polymorphism, non-coding RNA, messenger RNA, gene expression, isoform and alternative splicing information. Systems biology on the other hand studies complex biological systems, particularly systematic study of complex molecular interactions within specific cells or organisms. Genomics and molecular systems biology can be merged into the study of genomic profiles and implicated biological functions at cellular or organism level. The prospectively emerging field can be referred to as systems genomics or genomic systems biology.

Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable.

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Diabetes mellitus of type 2 (T2D), also known as noninsulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, is a common disease. It is estimated that more than 300 million people worldwide suffer from T2D. In this study, we investigated the T2D, pre-diabetic and healthy human (no diabetes) bloodstream samples using genomic, genealogical, and phonemic information. We identified differentially expressed genes and pathways. The study has provided deeper insights into the development of T2D, and provided useful information for further effective prevention and treatment of the disease.

Analyzing gene expression data from DNA microarrays by commonly used classifiers is a hard task, be-cause there are only a few observations but with thousands of measured genes in the data set. Partial least squares based dimension reduction (PLSDR) is superior to handling such high dimensional problem, but irrelevant features will introduce errors into the dimension reduction process and reduce the classification accuracy of learning machines. Here feature selection is applied to filter the data and an algorithm named PLSDR^g is described by integrating PLSDR with gene selection, which can effectively improve classification accuracy of learning machines. Feature selection is performed by the indication of t-statistics scores on standardized probes. Experimental results on seven microarray data sets show that the proposed method PLSDRg is effective and reliable to improve the generalization performance of classifiers.

Several artificial intelligent methods, including support vector regression (SVR), artificial neural networks (ANNs), and partial least square (PLS) are used for the multivariate calibration in the determination of the three aromatic amino acids (phenylalanine, tyrosine and tryptophan) in their mixtures by fluorescence spectroscopy. The results of the leave-one-out method show that SVR perform better than other methods, and appear to be good methods for this task. Furthermore, feature selection is performed for SVR to remove redundant features and a novel algorithm named PRIFER (prediction risk based feature selection for support vector regression) is proposed. Results on the above multivariate calibration data set show that PRIFER is a powerful tool for solving the multivariate calibration problems.

Predicting tumor malignancies is an important but difficult task. For many tumors, especially neural and endocrine tumors, traditional pathological and histological analyses often can not effectively distinguish benign from malignant tumors. Developing synergistic bioinformatics and computational intelligence system is effective, because deterministic cancer markers do not always exist in individual patients. We proposed a parallel paradigm of cancer and use a number of ensemble methods including boosting, bagging and consensus networking, and have designed a novel classification scheme that advantageously combines several computational intelligence algorithms such as the variants of self-organizing feature map (SOFM) algorithms and the maximum contrast tree (RMCT) algorithms. Boosting and bagging have been advantageously combined. When all of the above are integrated into one synergistic intelligent medical decision system, the prediction power for the task has been significantly boosted. The system and features are validated by diagnosing new patients and by a number of laboratory molecular biology measurements. The outcomes of the research have improved cancer diagnosis and treatment planning, and may lead to diagnose microscopic diseases and better understanding of human genome mechanisms relating to malignant transformation

Advances in high-throughput genome sequencing technology have led to an explosion in the amount of sequence data that are available. The determination of protein function using experimental techniques is time-consuming and expensive; the use of machine-learning techniques rapidly to assess protein function may be useful in streamlining this process. The problem of assigning functional classes to proteins is complicated by the fact that a single protein can participate in several different pathways and thus can have multiple functions. We have developed a tree-based classifier that is capable of handling multiple-labelled data and gaining an insight into the multi-functional nature of proteins. We call the resulting tree a recursive maximum contrast tree (RMCT) and the resulting classifier a multiple-labelled instance classifier (MLIC). We investigate the synergy of machine-learning-based ensemble methods and physiochemical-based feature augments. We test our algorithm on protein phylogenetic profiles generated from 60 completely sequenced genomes and we compare our results with those achieved by algorithms such as support vector machines and decision trees.