Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data

[[abstract]]Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. Time-series microarray data are gene expression values generated from microarray experiments within certain time intervals. Scientists can infer gene regulations in a biological system by judging whether two genes present similar gene expression values in microarray time-series data. Recently, a great many methods are widely applied on microarray time-series data to find out the similarity and the correlation degree among genes. Existing approaches including traditional Pearson coefficient correlation, Bayesian networks, clustering analysis, classification methods, and correlation analysis have individual disadvantages such as high computational complexity or they may be unsuitable for some microarray data. Traditional Pearson correlation coefficient is a numeric measuring method which gives novel effectiveness on two sets of numeric data. However, it is not suitable to be applied on microarray time-series data because of the existence of outliers among gene expression values. This paper presents a novel method of applying Pearson correlation coefficient along with an outlier filtering procedure on the widely-used microarray time-series datasets. Results show that the proposed method produces a better outcome compared with traditional Pearson correlation coefficient on the same dataset. Results show that the proposed method not only can find out certain more known regulatory gene pairs, but also keeps rational computational time.[[conferencetype]]國際[[conferencedate]]20090316~20090319[[iscallforpapers]]Y[[conferencelocation]]Fukuoka, Japa

Imputing Missing Values in Microarray Data with Ontology Information

[[abstract]]Microarray technology is a big step in bioinformatics. Hidden information within the large amounts of data provides scientists with molecular functions or essential biological meanings to study and analyze. However, these data often contain a certain portion of entities that are missing. Several methods to estimate these missing values are developed, but most of them are with disadvantages. In this paper, we propose a novel approach to deal with these missing values based on a practical similarity measurement between gene pairs. Our approach takes gene expression values and gene ontology (GO) information for genes into consideration. We implement our approach on a real microarray dataset and compare its imputation accuracy with other methods. Experimental results show that our approach can estimate missing values in microarray data effectively.[[conferencetype]]國際[[conferencedate]]20101218~20101221[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

A Comparison of Formal Methods for Evaluating the Language of Preference in Engineering Design

In design, as with many fields, the bases of decisions are generally not formally modeled but only talked or written about. The research problem addressed in this paper revolves around the problem of modeling the direct evaluation of design alternatives and their attributes as they are realized in linguistic communication. The question is what types of linguistic data provide the most reliable linguistic displays of preference and utility. The paper compares two formal methods for assessing a design team’s preferences for alternatives based on the team’s discussion: APPRAISAL and Preferential Probabilities from Transcripts (PPT). Results suggest that the two methods are comparable in their assessment of preferences. This paper also examines the nature of consistency in the way design teams consider the attributes of a design. Findings suggest that assessment of an attribute can change substantially over time.National Science Foundation (U.S.) (Award CMMI- 0900255)Australian Research Council (Discovery Projects funding scheme (project number DP1095601)

Applying Gene Ontology to Microarray Gene Expression Data Analysis

[[conferencetype]]國際[[conferencedate]]20100701~20100703[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Experimental Study on Condition Monitoring of Low Speed Bearings : Time Domain Analysis

In condition monitoring of low speed rolling element bearings (REBs), traditional techniques involving vibration acceleration may not be able to detect a growing fault due to the low impact energy generated by the relative motion of the components. This study presents an experimental evaluation for incipient fault detection of low speed REBs by using an acoustic emission (AE) sensor and an accelerometer. A low speed fault simulation test rig was developed to simulate common machine faults with shaft speeds as low as 10rpm under loading conditions. Tests were conducted on the rig with various seeded defect bearings. This study reveals the best frequency bandwidth and suitable parameters for condition monitoring using AE signal for early detection of low speed bearing defects by means of statistical parameters in time domain

Evaluating the Shelf Life and Sensory Properties of Beef Steaks from Cattle Raised on Different Grass Feeding Systems in the Western United States

Consumer interest in grass-fed beef has been steadily rising due to consumer perception of its potential benefits. This interest has led to a growing demand for niche market beef, particularly in the western United States. Therefore, the objective of this study was to assess the impact of feeding systems on the change in microbial counts, color, and lipid oxidation of steaks during retail display, and on their sensory attributes. The systems included: conventional grain-fed (CON), 20 months-grass-fed (20GF), 25-months-grass-fed (25GF) and 20-months-grass-fed + 45-day-grain-fed (45GR). The results indicate that steaks in the 20GF group displayed a darker lean and fat color, and a lower oxidation state than those in the 25GF group. However, the feeding system did not have an impact on pH or objective tenderness of beef steaks. In addition, consumers and trained panelist did not detect a difference in taste or flavor between the 20GF or 25GF steaks but expressed a preference for the CON and 45GR steaks, indicating that an increased grazing period may improve the color and oxidative stability of beef, while a short supplementation with grain may improve eating quality

Visual Servoing of Humanoid Dual-Arm Robot with Neural Learning Enhanced Skill Transferring Control

This paper presents a novel combination of visual servoing (VS) control and neural network (NN) learning on humanoid dual-arm robot. A VS control system is built by using stereo vision to obtain the 3D point cloud of a target object. A least square-based method is proposed to reduce the stochastic error in workspace calibration. An NN controller is designed to compensate for the effect of uncertainties in payload and other parameters (both internal and external) during the tracking control. In contrast to the conventional NN controller, a deterministic learning technique is utilized in this work, to enable the learned neural knowledge to be reused before current dynamics changes. A skill transfer mechanism is also developed to apply the neural learned knowledge from one arm to the other, to increase the neural learning efficiency. Tracked trajectory of object is used to provide target position to the coordinated dual arms of a Baxter robot in the experimental study. Robotic implementations has demonstrated the efficiency of the developed VS control system and has verified the effectiveness of the proposed NN controller with knowledge-reuse and skill transfer features

A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms ( SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds ( a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines - in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases

Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study.

BACKGROUND: Genome-wide association studies have identified >30 common SNPs associated with epithelial ovarian cancer (EOC). We evaluated the combined effects of EOC susceptibility SNPs on predicting EOC risk in an independent prospective cohort study. METHODS: We genotyped ovarian cancer susceptibility single nucleotide polymorphisms (SNPs) in a nested case-control study (750 cases and 1428 controls) from the UK Collaborative Trial of Ovarian Cancer Screening trial. Polygenic risk scores (PRSs) were constructed and their associations with EOC risk were evaluated using logistic regression. The absolute risk of developing ovarian cancer by PRS percentiles was calculated. RESULTS: The association between serous PRS and serous EOC (OR 1.43, 95% CI 1.29 to 1.58, p=1.3×10-11) was stronger than the association between overall PRS and overall EOC risk (OR 1.32, 95% CI 1.21 to 1.45, p=5.4×10-10). Women in the top fifth percentile of the PRS had a 3.4-fold increased EOC risk compared with women in the bottom 5% of the PRS, with the absolute EOC risk by age 80 being 2.9% and 0.9%, respectively, for the two groups of women in the population. CONCLUSION: PRSs can be used to predict future risk of developing ovarian cancer for women in the general population. Incorporation of PRSs into risk prediction models for EOC could inform clinical decision-making and health management