Gotowa bibliografia na temat „Bioinformatics and biostatistics”

Abstract Summary In biological assays, systematic variability, known as a batch effect, can often confound the effects of true biological conditions and has been well documented for a variety of high-throughput technologies. In microplate-based multiplex experiments, such as Luminex or OLINK assays, researchers need to consider both position and plate effects. Those effects can be easily accounted for if the experiments are properly designed, which includes randomization of the samples across multiple experimental runs. However, doing the ad hoc randomization becomes challenging when handling multiple samples. PlateDesigner is the first web-based application that provides randomization for microplate experiments, ensuring that the main principles of the experimental design, such as grouping samples from the same biological units and balancing the distribution of experimental conditions, are applied. Creating randomizations with PlateDesigner is simple and the results can be exported in a variety of formats, and easily integrated with microplate readers and statistical analysis software. Availability and implementation PlateDesigner is written in R/Shiny and is hosted online by the Center of Biostatistics at the Icahn School of Medicine at Mount Sinai. This application is freely available at platedesigner.net.

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Statistical gene detection plays an important role in biostatistics and bioinformatics. So far, many gene loci associated with human complex disease have been found by statistical methods. However, it is difficult to find all the mutation genes that are associated with a certain disease. Researchers need to detect more associated genes aiming at a disease so that human will conquer the disease one day. In this paper, we considered a real and big data set and study the detection problem of genes associated with the PIK3C2B gene on lung disease. 168 significant genes associated with the PIK3C2B gene were detected at nominal significance level 0.001 by using statistical multiple testing method. The detected genes will provide some reference to further study the function of the PIK3C2B gene to lung disease for biologists and medical scientists.

"guestProfessor Søren M. Bentzen is the Director of the Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, USA, and he is also an Adjunct Professor of Radiobiology and Medical Physics, University of Copenhagen, Denmark. Dr. Bentzen received a Master of Sciences in physics and mathematics, a PhD in medical image analysis, and he is a doctor of medical science in quantitative clinical radiobiology. He was one of the leaders of the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) initiative, and serves as a member of the Pediatric Normal Tissue Effects in the Clinic (PENTEC) steering committee. He was also a member of the American Society for Radiation Oncology (ASTRO) Task Forces to develop evidence-based guidelines on the appropriate fractionation for whole breast irradiation and for localized prostate cancer. He has served as chair or member of 17 (seven currently active) Independent Data and Safety Monitoring Committees or Trial Steering Committees and as co-chair for Translational Research for two Radiation Therapy Oncology Group (RTOG) phase III trials. He has published more than 500 original papers and book chapters, has presented >370 invited lectures, and he currently serves on 10 international cancer journal editorial boards. His main research interests include bioeffect modeling; biomathematics; applied biostatistics; clinical trial design; evidence-based medicine; late effects of radiotherapy; clinical radiobiology; integration of data from genomics, proteomics, and molecular imaging into novel therapeutic"

A computational evolution system (CES) is a knowledge-discovery engine that constructs and evolves classifiers with a small number of features to identify subtle, synergistic relationships among features and to discriminate groups in high-dimensional data analysis. CESs have previously been designed to only analyze binary datasets. In this work, the CES method has been expanded to accommodate multi-class data.

The multi-class CES was compared to three common classification and feature selection methods: random forest, random k-nearest neighbor, and support vector machines. The four classifiers were evaluated on three real RNA sequencing datasets. Performance was evaluated via cross validation to assess classification accuracy, number of features selected, stability of the selected feature sets, and run-time.

The three common classification and feature selection methods were originally designed for microarray data, which is fundamentally different from RNA-Seq data. In order to preprocess RNA-Seq count data for classification, the data was normalized and transformed via a variance stabilizing transformation to remove the variance-mean relationship that is commonly observed in RNA-Seq count data.

Compared to the three competing methods, the multi-class CES selected far fewer features. The identified features are potential biomarkers that may be more relevant than the longer lists of features identified by the competing methods. The CES performed best on the dataset with the smallest sample size, indicating that it has a unique advantage in these situations since most classification algorithms suffer in terms of accuracy when the sample size is small.

The CES identified numerous potentially-important biomarkers in each of the three real datasets that are validated by previous research and worthy of additional investigation. CES was especially helpful at identifying important features in the rat blood RNA-Seq data set. Subsequent ontological analysis of these selected features revealed protein folding as an important process in that dataset. The other contribution of this research to science was to extend the applicability of CES to biomarker discovery in multi-class settings. New software algorithms based on CES have already been developed, and the multi-class modifications presented here are directly applicable and would also benefit the newer software.

The rapid development of novel experimental techniques has led to the generation of an abundance of biological data, which holds great potential for elucidating many scientific problems. The analysis of such complex heterogeneous information, which we often have to deal with, requires appropriate state-of-the-art analytical methods. Here we demonstrate how an unconventional approach and intelligent data processing can lead to meaningful results.

This work includes three major parts. In the first part we describe a correction methodology for genome-wide association studies (GWAS). We demonstrate the existing bias for the selection of larger genes for downstream analyses in GWA studies and propose a method to adjust for this bias. Thus, we effectively show the need for data preprocessing in order to obtain a biologically relevant result. In the second part, building on the results obtained in the first part, we attempt to elucidate the underlying mechanisms of aging and longevity by conducting a longevity GWAS. Here we took an unconventional approach to the GWAS analysis by applying the idea of genetic buffering. Doing this allowed us to identify pairs of genetic markers that play a role in longevity. Furthermore, we were able to confirm some of them by means of a downstream network analysis. In the third and final part, we discuss the characteristics of chronic lymphocytic leukemia (CLL) B-cells and perform clustering analysis based on immunoglobulin (Ig) mutation patterns. By comparing the sequences of Ig of CLL patients and healthy donors, we show that different Ig heavy chain (IGHV) regions in CLL exhibit similarities with different B-cell subtypes of healthy donors, which raised a question about the single origin of CLL cases.

In the era of genomics, data analysis models and algorithms that provide the means to reduce large complex sets into meaningful information are integral to further our understanding of complex biological systems. Hidden Markov models comprise one such data analysis technique that has become the basis of many bioinformatics tools. Its relative success is primarily due to its conceptually simplicity and robust statistical foundation. Despite being one of the most popular data analysis modeling techniques for classification of linear sequences of data, researchers have few available software options to rapidly implement the necessary modeling framework and algorithms. Most tools are still hand-coded because current implementation solutions do not provide the required ease or flexibility that allows researchers to implement models in non-traditional ways. I have developed a free hidden Markov model C++ library and application, called StochHMM, that provides researchers with the flexibility to apply hidden Markov models to unique sequence analysis problems. It provides researchers the ability to rapidly implement a model using a simple text file and at the same time provide the flexibility to adapt the model in non-traditional ways. In addition, it provides many features that are not available in any current HMM implementation tools, such as stochastic sampling algorithms, ability to link user-defined functions into the HMM framework, and multiple ways to integrate additional data sources together to make better predictions. Using StochHMM, we have been able to rapidly implement models for R-loop prediction and classification of methylation domains. The R-loop predictions uncovered the epigenetic regulatory role of R-loops at CpG promoters and protein coding genes 3' transcription termination. Classification of methylation domains in multiple pluripotent tissues identified epigenetics gene tracks that will help inform our understanding of epigenetic diseases.

Human disease is complex. However, the explosion of biomedical data is providing new opportunities to improve our understanding. My dissertation focused on how to harness the biodata revolution. Broadly, I addressed three questions: how to integrate data, how to extract insights from data, and how to make science more open.

To integrate data, we pioneered the hetnet—a network with multiple node and relationship types. After several preludes, we released Hetionet v1.0, which contains 2,250,197 relationships of 24 types. Hetionet encodes the collective knowledge produced by millions of studies over the last half century.

To extract insights from data, we developed a machine learning approach for hetnets. In order to predict the probability that an unknown relationship exists, our algorithm identifies influential network patterns. We used the approach to prioritize disease—gene associations and drug repurposing opportunities. By evaluating our predictions on withheld knowledge, we demonstrated the systematic success of our method.

After encountering friction that interfered with data integration and rapid communication, I began looking at how to make science more open. The quest led me to explore realtime open notebook science and expose publishing delays at journals as well as the problematic licensing of publicly-funded research data.

petal is a network analysis method that includes and takes advantage of precise Mathematics, Statistics, and Graph Theory, but remains practical to the life scientist. petal is built upon the assumption that large complex systems follow a scale-free and small-world network topology. One main intention of creating this program is to eliminate unnecessary noise and imprecision introduced by the user. Consequently, no user input parameters are required, and the program is designed to allow the two structural properties, scale-free and small-world, to govern the construction of network models.

The program is implemented in the statistical language R and is freely available as a package for download. Its package includes several simple R functions that the researcher can use to construct co-expression networks and extract gene groupings from a biologically meaningful network model. More advanced R users may use other functions for further downstream analyses, if desired.

The petal algorithm is discussed and its application demonstrated on several datasets. petal results show that the technique is capable of detecting biologically meaningful network modules from co-expression networks. That is, scientists can use this technique to identify groups of genes with possible similar function based on their expression information.

While this approach is motivated by whole-system gene expression data, the fundamental components of the method are transparent and can be applied to large datasets of many types, sizes, and stemming from various fields.

Modern next-generation sequencing and microarray-based assays have empowered the computational biologist to measure various aspects of biological activity. This has led to the growth of genomics, transcriptomics and proteomics as fields of study of the complete set of DNA, RNA and proteins in living cells respectively. One major challenge in the analysis of this data, however, has been the widespread lack of sufficiently large sample sizes due to the high cost of new emerging technologies, making statistical inference difficult. In addition, due to the hierarchical nature of the various types of data, it is important to correctly integrate them to make meaningful biological discoveries and better informed decisions for the successful treatment of disease. In this dissertation I propose: (1) a novel method for more powerful statistical testing of differential digital gene expression between two conditions, (2) a framework for the integration of multi-level biologic data, demonstrated with the compositional analysis of gene expression and its link to promoter structure, and (3) an extension to a more complex generalized linear modeling framework, demonstrated with the compositional analysis of gene expression and its link to pathway structure adjusted for confounding covariates.

The BRCA1 and BRCA2 (BRCA) genes are two tumor suppressors that when mutated, predispose patients to breast and ovarian cancer. The BRCA genes encode proteins that mediate the repair of DNA double strand breaks. Functional loss of the BRCA genes is detrimental to the integrity of the genome because without access to functional BRCA protein, inefficient and error-prone repair pathways are used instead. These pathways, such as Non-homologous end joining, do not accurately repair the DNA, which can introduce mutations and genomic rearrangements. Ultimately the genome is not repaired faithfully and the predisposition to cancer greatly increases. In addition to their contribution to DNA repair, the BRCA genes have been shown to have transcriptional activity, and this functional role can also be a driving factor behind the tumor suppressor activity.

Robustness is the ability of a complex system to sustain viability despite perturbations to it. In the context of a complex disease such as cancer, robustness gives cancers the ability to sustain uncontrollable growth and invasiveness despite treatments such as chemotherapy that attempt to eliminate the tumor. A complex system is robust however can be fragile to perturbations that the system not optimized against. In cancers, these fragilities have the potential to be cancer specific targets that can eradicate the disease specifically.

Patients with mutations in BRCA tend to have breast and ovarian cancers that are difficult to treat; chemotherapy is the only option and no targeted therapies are available. Targeting the synthetic lethal interaction (SLI), a mechanism of robustness, between BRCA and PARP1 genes was clinically effective in treating BRCA-mutated breast and ovarian cancers. This suggests that understanding robustness in cancers can reveal potential cancer specific therapies.

In this thesis, a computational approach was developed to identify candidate mechanisms of robustness in BRCA-mutated breast and ovarian cancers using the publicly accessible patient gene expression and mutation data from the Cancer Genome Atlas (TCGA). Results showed that in ovarian cancer patients with a BRCA2 mutation, the expression of genes that function in the DNA damage response were kept at stable expression state compared to those patients without a mutation. The stable expression of genes in the DNA damage response may highlight a SLI gene network that is precisely controlled. This result is significant as disrupting this precision can potentially lead to cancer specific death. In breast cancers, genes that were differentially expressed in patients with BRCA mutations were identified. A Bayesian network was performed to infer candidate interactions between BRCA1 and BRCA2 and the differentially expressed FLT3, HOXA11, HPGD, MLF1, NGFR, PLAT, and ZBTB16 genes. These genes function in processes important to cancer progression such as apoptosis and cell migration. The connection between these genes with BRCA may highlight how the BRCA genes influence cancer progression.

Taken together, the findings of this thesis enhance our understanding of the BRCA genes and their role in DNA damage response and transcriptional regulation in human breast and ovarian cancers. These results have been attained from systems-level models to identify candidate mechanisms underlying robustness of cancers. The work presented predicts interesting candidate genes that may have potential as drug targets or biomarkers in BRCA-mutated breast and ovarian cancers.

This paper presents mathematical models that estimate nonlinear evolution processes or phenomena based on parameters that define processes and phenomena in pursuit of calculations and approximations (fitting) of experimental data. It addresses the non-linear model (nonlinear regression), the method of Least Squares (MLS) with examples. Are presented theoretical calculations for logarithmic and exponential models realizing a comparison with results obtained using software. In practice in various scientific, economic, social, etc.. occur most complex problems to be solved. Science and scientific research have developed influenced by the complexity of these issues and human society projects. Treasury of human knowledge is influenced by science and technology, culture and art, and in particular how to solve unsolved problems of human society. Such problems arise in chemistry, biology and medicine, physics and geology, in economics and sociology, etc.. To study and analyze processes and phenomena such activities require valid and effective methods and techniques so that models used to eliminate as many uncertainties and approximations. In practice, the study of various processes and phenomena is a wide variety of nonlinear models. The diversity of these nonlinear models depends on the variety fields of chemistry, physics, medicine, biology, sociology, economics, etc. Mathematics and Computer Science have changed dramatically analiya project on methods and analysis of experimental data. Chemistry, biology, pharmacokinetics are areas / disciplines have greatly benefited from the development of theories, methods and techniques of Mathematics and Computer Science via computer. Today procedures for drug testing include important results obtained in research on drug use in the treatment of various diseases. Bioinformatics, Biostatistics and Biopharmaceutics are disciplines that provide various methods and analysis on the pharmacokinetics.