Accessibility engineering in web evaluation process: a systematic literature review.

Several works of literature contributed to the web evaluation process in recent years to promote digital inclusion by addressing several accessibility guidelines, methods, processes, and techniques. Researchers have investigated how the web evaluation process could be facilitated by including accessibility issues to obtain an inclusive and accessible solution to improve the user experience and increase user satisfaction. Three systematic literature reviews (SLRs) have been conducted in the context of past research, considering such research focuses. This paper presents a new SLR approach concerning accessibility in the web evaluation process, considering the period from 2010 to 2021. The review of 92 primary studies showed the contribution of publications on different phases of the web evaluation process mainly by highlighting the significant studies in the framework design and testing process. To the best of our knowledge, this is the first study focused on the web accessibility literature reporting the engineering assets for evaluation of new accessible and inclusive web-based solutions (e.g., websites). Besides, in this study, we aim to provide a new direction to the web designers and developers with an updated view of process, methods, techniques, tools, and other crucial aspects to contribute to the accessible process enrichment, as well as depict the gaps and challenges that may be worthy to be investigated in the future. The findings of this SLR introduce a new dimension in web accessibility research on determining and mitigating the research gap of web accessibility issues for web designers, developers, and other practitioners.

Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications.

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies.

Reproducibility of disease signatures and clinical biomarkers in multi-omics disease analysis has been a key challenge due to a multitude of factors. The heterogeneity of the limited sample, various biological factors such as environmental confounders, and the inherent experimental and technical noises, compounded with the inadequacy of statistical tools, can lead to the misinterpretation of results, and subsequently very different biology. In this paper, we investigate the biomarker reproducibility issues, potentially caused by differences of statistical methods with varied distribution assumptions or marker selection criteria using Mass Spectrometry proteomic ovarian tumor data. We examine the relationship between effect sizes, p values, Cauchy p values, False Discovery Rate p values, and the rank fractions of identified proteins out of thousands in the limited heterogeneous sample. We compared the markers identified from statistical single features selection approaches with machine learning wrapper methods. The results reveal marked differences when selecting the protein markers from varied methods with potential selection biases and false discoveries, which may be due to the small effects, different distribution assumptions, and p value type criteria versus prediction accuracies. The alternative solutions and other related issues are discussed in supporting the reproducibility of findings for clinical actionable outcomes.

Type I Diabetes Self-management With Game-Based Interventions for Pediatric and Adolescent Patients.

Type 1 diabetes is a disease with a peak diagnosis between the ages of 10 and 14 and carries with it required intensive lifestyle changes. Disease self-management is essential for adequate metabolic control to prevent acute and long-term complications. Yet common methods of diabetes self-management education, such as lectures or pamphlets, lead to low knowledge, engagement, and clinical outcomes. Game-based learning has led to increased motivation, engagement, and productivity overall with substantial increases in self-management of chronic diseases in children. The purpose of this article is to review and synthesize literature on the impact on self-management knowledge, behavior, and engagement of the game-based interventions of serious games and gamification for children and adolescents with type 1 diabetes. Nine studies were reviewed. Results showed statistically significant differences in knowledge, behavior, and engagement in response to the game-based interventions. Knowledge outcomes were found most significant in serious game interventions, while behavioral outcomes were predominantly found in gamification/serious game combination interventions. Findings also reveal there was inconsistent use of theories for game development and moderate to low quality of evidence across studies. While the nine studies reviewed strongly demonstrate the potential of game-based tools to significantly improve type 1 diabetes self-management care, further studies with expanded and more rigorous study parameters are recommended before an outright change in practice may be applied. The potential impact of the clinical nurse leader in the use and research of game-based interventions is also discussed.

Computer- and Robot-Assisted Therapies to Aid Social and Intellectual Functioning of Children with Autism Spectrum Disorder.

BACKGROUND AND OBJECTIVES: Children with autism spectrum disorder (ASD) experience challenges with social interactions, a core feature of the disorder. Social skills therapy has been shown to be helpful. Over the past several years, computer-assisted and robot-assisted therapies have been infiltrating the social skills teaching environment. Rapid progress in the field of technology, especially in the robotics area, offers tremendous possibilities for innovation and treatment or even education for individuals with ASD. This paper's purpose is to drive awareness of these innovative interventions in order to support the social lives of children with ASD. The aims of the paper are identifying (1) the types of Information Technology platforms that are being evaluated in computer and robot-assisted therapies for children with ASD; (2) the various disciplines or professions studying and utilizing these computer and robot-assisted social skill therapies; (3) the outcomes being evaluated in each trial; and (4) if results demonstrate benefits to children with autism. MATERIALS AND METHODS: PubMed, CINAHL, Science Direct, and Web of Science databases were searched for clinical trials published over the past five years. Search terms incorporated the subject intersection of autism, and computer or robot-assisted therapy. Results were mined for pediatric populations only and study designs establishing controlled comparisons. RESULTS: Eighteen unique international studies were identified that utilize robot interventions (11 studies) and serious computer game interventions (seven studies). Most demonstrated promising results in improving outcomes for children with ASD. Study implications reveal a rapidly evolving assistive technology for ASD social skills therapy. CONCLUSIONS: These interventions show considerable promise, but more effectiveness and cost effectiveness research of high quality should be carried out with larger numbers of children. Also, further studies are necessary to evaluate these technologies' effectiveness amongst adults with ASD and within unique subsets of the higher functioning autism population.

Creation and Evaluation of a Preoperative Education Website for Hip and Knee Replacement Patients-A Pilot Study.

The use of websites to provide patient education is becoming more common. The benefits of a properly executed and effective preoperative patient educational intervention have been shown to result in improved psychological and physical well-being for patients undergoing surgery. The purpose of this pilot study was to determine the usability, utility, and feasibility of a website we created to increase engagement and improve the quality of the preoperative education patients receive in preparation for hip and knee arthroplasty. Eighty patients who met the inclusion criteria were recruited, aged between 40 to 65, among those 52.5% were female, 71.25% were placed for knee replacement, 28.75% for hip replacement. Forty patients were randomly assigned to paper education cohort, 40 to the paper and website education cohort. However, only 19 from each cohort participated in the survey questionnaire. The outcome of interest included qualitative data for patient knowledge, satisfaction, utilities, and usability, which were assessed based on the Perceived Health Website Usability Questionnaire online survey. The paper-based survey contains ten questions using a 7-point Likert scale while the web-based survey contains fourteen questions using the same 7-point Likert scale. Descriptive statistics and independent samples t-tests were used for comparative analysis of usual paper education and website education cohorts; whereby Microsoft Excel data analytics tool was used to compute the results. The Alpha level was set to 0.05 for the statistical results. The result of the study showed no statistically significant differences in both cohorts at the 0.05 level. We hypothesized that both information delivery methods were effective in increasing knowledge and engaging patients to their preoperative educations. According to the survey result for the nursing staff, they believed that the use of the website improved nursing workflow, efficiency, and patient education.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Virtual reality gaming in the rehabilitation of the upper extremities post-stroke.

BACKGROUND: Occurrences of strokes often result in unilateral upper limb dysfunction. Dysfunctions of this nature frequently persist and can present chronic limitations to activities of daily living. METHODS: Research into applying virtual reality gaming systems to provide rehabilitation therapy have seen resurgence. Themes explored in stroke rehab for paretic limbs are action observation and imitation, versatility, intensity and repetition and preservation of gains. Fifteen articles were ultimately selected for review. The purpose of this literature review is to compare the various virtual reality gaming modalities in the current literature and ascertain their efficacy. RESULTS: The literature supports the use of virtual reality gaming rehab therapy as equivalent to traditional therapies or as successful augmentation to those therapies. While some degree of rigor was displayed in the literature, small sample sizes, variation in study lengths and therapy durations and unequal controls reduce generalizability and comparability. CONCLUSIONS: Future studies should incorporate larger sample sizes and post-intervention follow-up measures.

Turning electronic health record data into meaningful information using SQL and nursing informatics.

The combination of nursing informatics knowledge with SQL code writing in an electronic health record is a powerful partnership to obtain meaningful information and improve healthcare. The purpose of this project is to use SQL and nursing informatics to identify the underpinnings and scope of present-on-patient-admission pressure ulcer documentation incongruence within the inpatient medical-surgical unit of a rural hospital. Project results reveal a 76% incidence rate of incongruent nurse and physician documentation of pressure ulcers as present on admission. However, the scope of such incongruence affects only 3% for the inpatient population. The high incidence rate of nurse-documented present-on-admission pressure ulcers without a physician diagnoses indicates a potential for lost rural hospital reimbursement and risk to patient care.

Considerations for a successful clinical decision support system.

Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.

Use of Smartphone Apps for Improving Physical Function Capacity in Cardiac Patient Rehabilitation: Systematic Review.

BACKGROUND: Cardiac rehabilitation (CR) is an evidence-based approach for preventing secondary cardiac events. Smartphone apps are starting to be used in CR to give patients real-time feedback on their health, connect them remotely with their medical team, and allow them to perform their rehabilitation at home. The use of smartphone apps is becoming omnipresent and has real potential in impacting patients in need of CR. OBJECTIVE: This paper provides critical examinations and summaries of existing research studies with an in-depth analysis of not only the individual studies but also the larger patterns that have emerged with smartphone apps in CR as well as their significance for practice change. METHODS: A systematic review was conducted through broad database searches that focused on evaluating randomized controlled trials, in compliance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) expectations. A total of 43 articles were evaluated, and 6 were chosen for this review. The dates of the articles ranged from 2014-2020, and the studies focused on the population of cardiac outpatients who needed CR after suffering a cardiac event, with interventions using a smartphone that incorporated the CR standards of the American Heart Association. The outcomes measured were directed at focusing on improved exercise function capacity, valued at a significance level of P<.05, for improved 6-minute walk test (6MWT) and peak oxygen uptake (PVO2) results. RESULTS: In the evaluated articles, the results were inconsistent for significant positive effects of CR smartphone apps on cardiac patients' physical function capacity in terms of the 6MWT and PVO2 when using a smartphone app to aid in CR. CONCLUSIONS: Because evidence in the literature suggests nonhomogeneous results for successful use of smartphone apps in CR, it is crucial to investigate the potential reasons for this inconsistency. An important observation from this systematic review is that smartphone apps used in CR have better clinical outcomes related to physical function capacity if the app automatically records information or provides real-time feedback to participants about their progress, compared to apps that only educate and encourage use while requiring the participant to manually log their CR activities. Additional factors to consider during these studies include the starting health of the patients, the sample sizes, and the specific components of CR that the smartphone apps are using. Overall, more clinical trials are needed that implement smartphone apps with these factors in mind, while placing stronger emphasis on using biosensing capabilities that can automatically log results and send them to providers on a real-time dashboard.

A Novel Marker Detection System for People with Visual Impairment Using the Improved Tiny-YOLOv3 Model.

BACKGROUND AND OBJECTIVE: Daily activities such as shopping and navigating indoors are challenging problems for people with visual impairment. Researchers tried to find different solutions to help people with visual impairment navigate indoors and outdoors. METHODS: We applied deep learning to help visually impaired people navigate indoors using markers. We propose a system to help them detect markers and navigate indoors using an improved Tiny-YOLOv3 model. A dataset was created by collecting marker images from recorded videos and augmenting them using image processing techniques such as rotation transformation, brightness, and blur processing. After training and validating this model, the performance was tested on a testing dataset and on real videos. RESULTS: The contributions of this paper are: (1) We developed a navigation system to help people with visual impairment navigate indoors using markers; (2) We implemented and tested a deep learning model to detect Aruco markers in different challenging situations using Tiny-YOLOv3; (3) We implemented and compared several modified versions of the original model to improve detection accuracy. The modified Tiny-YOLOv3 model achieved an accuracy of 99.31% in challenging conditions and the original model achieved an accuracy of 96.11 %. CONCLUSION: The training and testing results show that the improved Tiny-YOLOv3 models are superior to the original model.

A Physical Activity Mobile Game for Hematopoietic Stem Cell Transplant Patients: App Design, Development, and Evaluation.

BACKGROUND: Physical activity mobile apps may encourage patients with cancer to increase exercise uptake, consequently decreasing cancer-related fatigue. While many fitness apps are currently available for download, most are not suitable for patients with cancer due to the unique barriers these patients face, such as fatigue, pain, and nausea. OBJECTIVE: The aim of this study is to design, develop, and perform alpha testing of a physical activity mobile health game for hematopoietic stem cell transplant (HSCT) patients. The ultimate future goal of this project is to motivate HSCT patients to increase physical activity and provide them with a safe and fun way to exercise. METHODS: A mobile health game called Walking Warrior was designed as a puzzle game where tiles are moved and matched. Walking Warrior interfaces with an open-source step counter and communicates with a central online MySQL database to record game play and walking performance. The game came to fruition after following an iterative process model with several prototypes. Game developers and bone marrow transplant nurses were recruited to perform an expert usability evaluation of the Walking Warrior prototype by completing a heuristic questionnaire and providing qualitative suggestions for improvement. Experts also made qualitative recommendations for improvements on speed, movement of tiles, appearance, and accuracy of the step counter. We recruited 5 additional usability evaluators who searched for and compared 4 open-source step counter programs, then qualitatively compared them for accuracy, robustness, cheat proofing, ease of use, and battery drain issues. Patient recruitment is planned at a later stage in this project. This paper only describes software design, development, and evaluation, rather than behavioral evaluation (ie, impact on physical activity), which is the long-term goal of this project. RESULTS: Internal consistency and the instrument's reliability evaluation results from 1 clinical expert and 4 technical experts were deemed excellent (Cronbach α=.933). A hierarchical cluster analysis of the questionnaire item responses for similarity/dissimilarity among the experts indicated that the two expert groups were not clustered into two separate groups in the dendrogram. This indicates that the item responses were not affected by profession. Factor analyses indicate that responses from the 40-item questionnaire were classified into five primary factors. The associated descriptive statistics for each of these categories were as follows (on a scale of 1 to 5): clarity and ease (median 4; mean 3.7, SD 0.45), appropriateness (median 4; mean 3.7, SD 0.49), game quality (median 3.5; mean 3.3, SD 0.42), motivation to walk (median 3; mean 3.1, SD 0.58), and mental effort (median 3.5; mean 3.1, SD 1.27). CONCLUSIONS: The evaluation from experts and clinicians provided qualitative information to further improve game design and development. Findings from the expert usability evaluation suggest the game's assets of clarity, ease of use, appropriateness, quality, motivation to walk, and mental effort were all favorable. This mobile game could ultimately help patients increase physical activity as an aid to recovery.

Use of supplementary phenotype to identify additional rheumatoid arthritis loci in a linkage analysis of 342 UK affected sibling pair families.

BACKGROUND: Although rheumatoid arthritis has been shown to have moderately strong genetic component, both linked loci identified in linkage analyses and susceptibility variants from association studies are short of adequately accounting for a comprehensive catalogue of the molecular factors underlying this complex disease. The objective of this study was to use supplementary phenotype based on cumulative hazard of rheumatoid arthritis to identify linkage evidence for new and additional rheumatoid arthritis loci in a genome-wide linkage analysis of 342 affected sibling pair families from the United Kingdom. METHODS: Using proportional hazards model, we estimated cumulative hazard of rheumatoid arthritis and then used it as a quantitative trait in a non-parametric multipoint variance component linkage analysis with 353 microsatellite markers distributed across the 22 autosomal chromosomes. RESULTS: We identified 3 new loci with genome-wide suggestive linkage evidence for rheumatoid arthritis on 9q21.13, 15p11.1 and 20q13.33. Our results also confirmed previously reported linkage evidence in the HLA-DRB1 region on chromosome 6 and on locus 1q32.1. CONCLUSION: This study demonstrates the potential for information gain through the use of supplementary phenotypes in genetic study of complex diseases to identify new and additional potential linked loci that are not detected by linkage analysis of traditional phenotypes; and our results provide further evidence of the involvement of multiple loci in the genetic aetiology of rheumatoid arthritis.

Brain atrophy and white matter hyperintensities in early Parkinson's disease(a).

The purpose of this research was to examine the extent of global brain atrophy and white matter hyperintensities (WMH) in early Parkinson's disease (PD) compared to normal controls (NC), to explore the relationship between the MRI variables and cognition in PD. In this multicenter study we included 155 PD patients (age 65.6 +/- 9.1 years, disease duration 26.7 +/- 19.9 months) and 101 age-matched NC. On 3D-T1-WI, we calculated normalized brain volumes using SIENAX software. WMH volumes were assessed semiautomatically. In PD patients, correlation and regression analyses investigated the association between atrophy and WMH outcomes and global, attention-executive, visuospatial, and memory cognitive functions. Regression analysis was controlled for age, education, depression score, motor severity, cerebrovascular risk, and sex. No significant MRI variable volume group differences were found. The models did not retain any of the imaging variables as significant predictors of cognitive impairment. There was no evidence of brain atrophy or higher WMH volume in PD compared to NC, and MRI volumetric measurements were not significant predictors of cognitive functions in PD patients. We conclude that global structural brain changes are not a major feature in patients with incident PD.

Bayesian finite Markov mixture model for temporal multi-tissue polygenic patterns.

Finite mixture models can provide the insights about behavioral patterns as a source of heterogeneity of the various dynamics of time course gene expression data by reducing the high dimensionality and making clear the major components of the underlying structure of the data in terms of the unobservable latent variables. The latent structure of the dynamic transition process of gene expression changes over time can be represented by Markov processes. This paper addresses key problems in the analysis of large gene expression data sets that describe systemic temporal response cascades and dynamic changes to therapeutic doses in multiple tissues, such as liver, skeletal muscle, and kidney from the same animals. Bayesian Finite Markov Mixture Model with a Dirichlet Prior is developed for the identifications of differentially expressed time related genes and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. The proposed Bayesian models are applied to multiple tissue polygenetic temporal gene expression data and compared to a Bayesian model-based clustering method, named CAGED. Results show that our proposed Bayesian Finite Markov Mixture model can well capture the dynamic changes and patterns for irregular complex temporal data.

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration.

BACKGROUND: This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. RESULTS AND CONCLUSION: Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

Augmented reality in neurosurgery.

Neurosurgery is a medical specialty that relies heavily on imaging. The use of computed tomography and magnetic resonance images during preoperative planning and intraoperative surgical navigation is vital to the success of the surgery and positive patient outcome. Augmented reality application in neurosurgery has the potential to revolutionize and change the way neurosurgeons plan and perform surgical procedures in the future. Augmented reality technology is currently commercially available for neurosurgery for simulation and training. However, the use of augmented reality in the clinical setting is still in its infancy. Researchers are now testing augmented reality system prototypes to determine and address the barriers and limitations of the technology before it can be widely accepted and used in the clinical setting.

Model-based or algorithm-based? Statistical evidence for diabetes and treatments using gene expression.

Gene expression profiles obtained from samples of diabetic and normal rats with and without treatments can be used to identify genes that distinguish normal and diabetic individuals and also to evaluate the effectiveness of drug treatments. This study examines changes in global gene expression in rat muscle caused by streptozotocin-induced diabetes and vanadyl sulfate treatment. We explored model-based and algorithm-based methods with gene screening measures for microarray gene expression data to classify and predict individuals with high risk of diabetes. Results show that the mixed ANOVA model-based approach provides an efficient way to conduct an investigation of the inherent variability in gene expression data and to estimate the effects of experimental factors such as treatments and diseases and their interactions. The algorithm-based weighted voting and neural network classifiers show good classification performance for the diabetes and treatment groups. Although neural network performs better than weighted voting with higher classification rate, the interpretation of weighted voting is more straightforward. The study indicates that the choice of the gene selection procedure is at least as important as the choice of the classification procedure. We conclude that both mixed model-based and algorithm-based approaches provide the statistical evidence of the biological hypotheses that vanadyl sulfate treatment of diabetic animals restores gene expression patterns to normal. Although model-based and algorithm-based methods provide different strengths and perspective for the analysis of the same set of data, in general both can be considered and developed for analyzing factorial design experiments with multiple groups and factors. This study represents a major step towards the discovery of responsible genes related to diabetes and its treatment.

Bayesian state space models for inferring and predicting temporal gene expression profiles.

Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models.