Finding edging genes from microarray data

Motivation: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs.

Result: We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm.

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.

A genomic approach for improved detection of annual ryegrass toxicity

Annual Ryegrass Toxicity is a severe and constant threat to the Australian agricultural industry. Current diagnostic and detection strategies to predict and monitor ARGT are limited. This thesis utilised genomic-based technologies to develop improved strategies for detection of molecular indicators of toxicity in field and livestock to facilitate pre-clinical detection.

A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

Highly selective and sensitive DNA assay based on electrocatalytic oxidation of ferrocene bearing zinc(II)-cyclen complexes with diethylamine

A highly selective and sensitive electrochemical biosensor has been developed that detects DNA hybridization by employing the electrocatalytic activity of ferrocene (Fc) bearing cyclen complexes (cyclen = 1,4,7,10-tetraazacyclododecane, Fc[Zn(cyclen)H2O]2(ClO4)4 (R1), Fc(cyclen)2 (R2), Fc[Zn(cyclen)H2O](ClO4)2 (R3), and Fc(cyclen) (R4)). A sandwich-type approach, which involves hybridization of a target probe hybridized with the preimmobilized thiolated capture probe attached to a gold electrode, is employed to fabricate a DNA duplex layer. Electrochemical signals are generated by voltammetric interrogation of a Fc bearing Zn−cyclen complexes that selectively and quantitatively binds to the duplex layers through strong chelation between the cyclen complexes and particular nucleobases within the DNA sequence. Chelate formation between R1 or R3 and thymine bases leads to the perturbation of base-pair (A−T) stacking in the duplex structure, which greatly diminishes the yield of DNA-mediated charge transport and displays a marked selectivity to the presence of the target DNA sequence. Coupling the redox chemistry of the surface-bound Fc bearing Zn−cyclen complex and dimethylamine provides an electrocatalytic pathway that increases sensitivity of the assay and allows the 100 fM target DNA sequence to be detected. Excellent selectivity against even single-base sequence mismatches is achieved, and the DNA sensor is stable and reusable.

Simplifying gene expression microarray comparative analysis.

Gene Expression Comparative Analysis allows bioinformatics researchers to discover the conserved or specific functional regulation of genes. This is achieved through comparisons between quantitative gene expression measurements obtained in different species on different platforms to address a particular biological system. Comparisons are made more difficult due to the need to map orthologous genes between species, pre-processing of data (normalization) and post-analysis (statistical and correlation analysis). In this paper we introduce a web-based software package called EXP-PAC which provides on line interfaces for database construction and query of data, and makes use of a high performance computing platform of computer clusters to run gene sequence mapping and normalization methods in parallel. Thus, EXP-PAC facilitates the integration of gene expression data for comparative analysis and the online sharing, retrieval and visualization of complex multi-specific and multi-platform gene expression results.

Simulation and analysis of a sub-wavelength grating based multilayer surface plasmon resonance biosensor

This paper presents a subwavelength grating based multilayer surface plasmon resonance biosensor (SPRB) which includes a periodic array of subwavelength grating on top of a layer of graphene sheet in the biosensor. The proposed biosensor is named grating-graphene SPRB (GG-SPRB). The aim of the proposed multilayer structure is to improve the sensitivity of the SPRB through monitoring of the biomolecular interactions of DNA hybridization. Significant sensitivity improvement is obtained for the GG-SPRB compared with the conventional SPRB. The result of the numerical investigation of the GG-SPRB is presented and compared with a theoretically developed multilayer matrix formalism, and a good agreement has been observed. In addition, an optimization of the grating dimensions including volume factor, grating depth, grating angle, grating period, and grating geometry (e.g., rectangular, sinusoidal and triangular) is presented. The outcome of the investigation presented in this paper identifies desired functioning conditions corresponding to the best design parameters for the GG-SPRB.

Peptide-based biosensors

Peptides have been used as components in biological analysis and fabrication of novel biosensors for a number of reasons, including mature synthesis protocols, diverse structures and as highly selective substrates for enzymes. Bio-conjugation strategies can provide an efficient way to convert interaction information between peptides and analytes into a measurable signal, which can be used for fabrication of novel peptide-based biosensors. Many sensitive fluorophores can respond rapidly to environmental changes and stimuli manifest as a change in spectral characteristics, hence environmentally-sensitive fluorophores have been widely used as signal markers to conjugate to peptides to construct peptide-based molecular sensors. Additionally, nanoparticles, fluorescent polymers, graphene and near infrared dyes are also used as peptide-conjugated signal markers. On the other hand, peptides may play a generalist role in peptide-based biosensors. Peptides have been utilized as bio-recognition elements to bind various analytes including proteins, nucleic acid, bacteria, metal ions, enzymes and antibodies in biosensors. The selectivity of peptides as an enzymatic substrate has thus been utilized to construct enzyme sensors or enzyme-activity sensors. In addition, progress on immobilization and microarray techniques of peptides has facilitated the progress and commercial application of chip-based peptide biosensors in clinical diagnosis.

A survey of cloud-based service computing solutions for mammalian genomics

Cloud-based service computing has started to change the way how research in science, in particular biology, medicine, and engineering, is being carried out. Researchers in the area of mammalian genomics have taken advantage of cloud computing technology to cost-effectively process large amounts of data and speed up discovery. Mammalian genomics is limited by the cost and complexity of analysis, which require large amounts of computational resources to analyse huge amount of data and biology specialists to interpret results. On the other hand the application of this technology requires computing knowledge, in particular programming and operations management skills to develop high performance computing (HPC) applications and deploy them on HPC clouds. We carried out a survey of cloud-based service computing solutions, as the most recent and promising instantiations of distributed computing systems, in the context their use in research of mammalian genomic analysis. We describe our most recent research and development effort which focuses on building Software as a Service (SaaS) clouds to simplify the use of HPC clouds for carrying out mammalian genomic analysis.

Numerical investigation of a grating and graphene-based multilayer surface plasmon resonance biosensor

A multilayer surface plasmon resonance biosensor (SPRB) incorporating a grating-graphene configuration is investigated for enhanced sensitivity. The numerical analysis of the impact of integrating a periodic array of subwavelength grating on top of a layer of graphene sheet for improving sensitivity is presented. The result of monitoring the biomolecular interactions of DNA hybridization is compared against the outcome of the conventional SPRB, a graphene-based multilayer SPRB, and a multilayer layer grating SPRB, and is mathematically validated. It is demonstrated that the inclusion of a grating and graphene layer on top of the gold thin film is an excellent candidate for a highly sensitive SPRB. To achieve further enhancement of sensitivity, the subwavelength grating is numerically optimized against its geometry including grating configurations (rectangular, sinusoidal, and triangular), grating depth, volume factor, and grating period. © 2014 Taylor & Francis.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

"Cancer 2015": a prospective, population-based cancer cohort-phase 1: feasibility of genomics-guided precision medicine in the clinic

"Cancer 2015" is a longitudinal and prospective cohort. It is a phased study whose aim was to pilot recruiting 1000 patients during phase 1 to establish the feasibility of providing a population-based genomics cohort. Newly diagnosed adult patients with solid cancers, with residual tumour material for molecular genomics testing, were recruited into the cohort for the collection of a dataset containing clinical, molecular pathology, health resource use and outcomes data. 1685 patients have been recruited over almost 3 years from five hospitals. Thirty-two percent are aged between 61-70 years old, with a median age of 63 years. Diagnostic tumour samples were obtained for 90% of these patients for multiple parallel sequencing. Patients identified with somatic mutations of potentially "actionable" variants represented almost 10% of those tumours sequenced, while 42% of the cohort had no mutations identified. These genomic data were annotated with information such as cancer site, stage, morphology, treatment and patient outcomes and health resource use and cost. This cohort has delivered its main objective of establishing an upscalable genomics cohort within a clinical setting and in phase 2 aims to develop a protocol for how genomics testing can be used in real-time clinical decision-making, providing evidence on the value of precision medicine to clinical practice.

Cancer 2015: a longitudinal whole-of-system study of genomic cancer medicine

Genomic cancer medicine promises revolutionary change in oncology. The impacts of 'personalized medicine', based upon a molecular classification of cancer and linked to targeted therapies, will extend from individual patient outcomes to the health economy at large. To address the 'whole-of-system' impact of genomic cancer medicine, we have established a prospective cohort of patients with newly diagnosed cancer in the state of Victoria, Australia, about whom we have collected a broad range of clinical, demographic, molecular, and patient-reported data, as well as data on health resource utilization. Our goal is to create a model for investigating public investment in genomic medicine that maximizes the cost:benefit ratio for the Australian community at large.

Functional genomic analysis of systemic cell division regulation in legumes

Legumes develop root nodules from pluripotent stem cells in the rootpericycle in response to mitogenic activation by a decorated chitin-likenodulation factor synthesized in Rhizobium bacteria. The soybean genes encoding the receptor for such signals were cloned using map-based cloning approaches. Pluripotent cells in the root pericycle and the outer or inner cortex undergo repeated cell divisions to initiate a composite nodule primordium that develops to a functional nitrogen-fixing nodule. The process itself is autoregulated, leading to the characteristic nodulation of the upper root system. Autoregulation of nodulation (AON) in all legumes is controlled in part by a leucine-rich repeat receptor kinase gene (GmNARK). Mutations of GmNARK, and its other legume orthologues, result in abundant nodulation caused by the loss of a yet-undefined negative nodulation repressor system. AON receptor kinases are involved in perception of a long distance, root-derived signal, to negatively control nodule proliferation. GmNARK and LjHAR1 are expressed in phloem parenchyma. GmNARK kinase domain interacts with Kinase Associated Protein Phosphatase (KAPP). NARK gene expression did not mirror biological NARK activity in nodulation control, as q-RT-PCR in soybean revealed high NARK expression in roots, root tips, leaves, petioles, stems and hypocotyls, while shoot and root apical meristems were devoid of NARK RNA. High through-put transcript analysis in soybean leaf and root indicated that major genes involved in JA synthesis or response are preferentially down-regulated in leaf but not root of wild type, but not NARK mutants, suggesting that AON signaling may in part be controlled by events relating to hormone metabolism. Ethylene and abscisic acid insensitive mutants of L. japonicus are described. Nodulation in legumes has significance to global economies and ecologies, as the nitrogen input into the biosphere allows food, feed and biofuel production without the inherent costs associated with nitrogen fertilization [1]. Nodulation involves the production of a new organ capable of nitrogen fixation [2] and as such is an excellent system to study plant – microbe interaction, plant development, long distance signaling and functional genomics of stem cell proliferation [3, 4]. Concerted international effort over the last 20 years, using a combination of induced mutagenesis followed by gene discovery (forward genetics), and molecular/biochemical approaches revealed a complex developmental pathway that ‘loans’ genetic programs from various sources and orchestrates these into a novel contribution. We report our laboratory’s contribution to the present analysis in the field.