Kinds Of Biological Data Research Articles

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I

Refining Protein Interaction Network for Identifying Essential Proteins

Aim: The study aimed to reconstruct the protein-protein interaction network for the identification of essential proteins. Background: In a living organism, essential proteins play an indispensable role in its survival and development. Hence, how to identify essential proteins from the protein interaction network (PIN) has become a hot topic in the field of bioinformatics. However, existing methods’ accuracies for identifying essential proteins are still limited due to the false positives of the protein-protein interaction data. Objective: The objective of the study was to propose an efficient algorithm for the reconstruction of a protein-protein interaction network. Methods: In this paper, a method for the refinement of PIN based on three kinds of biological data (subcellular localization data, protein complex data, and gene expression data) is proposed. Through evaluating each interaction within the original PIN, a refined clean PIN could be obtained. To verify the effectiveness of the refined PIN for the identification of essential proteins, we applied eight networkbased essential protein discovery methods (DC, BC, CC, LC, HC, SC, LAC, and NC) to it. Result: Based on the obtained experimental results, we demonstrated that the precision for identifying essential proteins could be greatly improved by refining the original PIN using our method. Conclusion: Our method could effectively enhance the protein-protein interaction network and improve the accuracy of identifying essential proteins. In the future, we plan to integrate more biological information to enhance our refinement method and apply it to more species and more PIN-based discovery tasks, like the identification of protein complexes or functional modules.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes.

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

A model for establishment, maintenance and reactivation of the immune response after vaccination against Ebola virus

The 2014–2016 Ebola outbreak in West Africa has triggered accelerated development of several preventive vaccines against Ebola virus. Under the EBOVAC1 consortium, three phase I studies were carried out to assess safety and immunogenicity of a two-dose heterologous vaccination regimen developed by Janssen Vaccines and Prevention in collaboration with Bavarian Nordic. To describe the immune response induced by the two-dose heterologous vaccine regimen, we propose a mechanistic ODE based model, which takes into account the role of immunological memory. We perform identifiability and sensitivity analysis of the proposed model to establish which kind of biological data are ideally needed in order to accurately estimate parameters, and additionally, which of those are non-identifiable based on the available data. Antibody concentrations data from phase I studies have been used to calibrate the model and show its ability in reproducing the observed antibody dynamics. Together with other factors, the establishment of an effective and reactive immunological memory is of pivotal importance for several prophylactic vaccines. We show that introducing a memory compartment in our calibrated model allows to evaluate the magnitude of the immune response induced by a booster dose and its long-term persistence afterwards.

A Secure-Ware System for Web Server: Ensuring Platform Interoperability, Security, Privacy, Usability and Functionality

Numerous software tools are available for systems biologist that primarily deals with the different kinds of biological data from the different kind of biological resources, such as micro-arrays, metabolic-networks and predicted protein-structure. A prime challenge of today is to commingle the data resources of the biological data; thus this letter designs a tool of secure-ware system to explore and analyze the data environment; and eventually the proposed system justifies that it can offer interactivity, scalability, flexibility, security and privacy to the complex system biological data sets. To provide the security, privacy, interoperability, usability and functionality, an efficient secure-ware system has been designed. Above all, the proposed system can support and recognize all types of data exchange formats and software to explore a wide range of biological data.

Identifying disease genes by integrating multiple data sources.

BackgroundNow multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes.ResultsIn this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes.ConclusionsNumerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.

An Effective Nonparametric Quantile Regression Method for Solving the Crossing Problem in Data Fitting Process

Nonparametric quantile regression method can be used as the first choice for some biostatistical data. Since the nonparametric quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution estimation for the response. A simple nonparametric quantile regression method is proposed to avoid the crossing problem. The method uses nonparametric conditional density function estimate instead of the conditional distribution estimate to assure quantile regression function monotonous. Both a simulation study and an analysis of real salmon lustrousness data show the significant improvement of the method in solving the quantile crossing problem for some kind of biological data.

TAXODIUM version 1.0: a simple way to generate uniform and fractionally weighted three-item matrices from various kinds of biological data.

An open-access program for generating three-item statement (3TS) matrices from data such as molecular sequences does not currently exist. The recently developed LisBeth package allows for representation of hypotheses of homology among taxa or areas directly as rooted trees or as hierarchies; however, LisBeth is not a standard matrix-based platform. Here we present “TAXODIUM version 1.0” (TAXODIUM), a program designed for building 3TS-matrices from binary, additive (ordered) and non-additive (unordered) multistate characters, with both uniform and fractional weighting of the statements. TAXODIUM also facilitates, for the first time, use of Maximum Likelihood analyses with 3TS matrices, but future implementation of the 3TS analysis in a statistical framework will require more exploration.

How i quantified myself

One warm night last February, I lay down to bed feeling like a lab mouse. A heat-and motion-sensing arm-band gauged my energy expenditure, another activity tracker clipped to my waistband recorded movement, a blood-pressure cuff connected to my iPad squeezed my right arm, and a brainwave-sensing headband would soon monitor my sleep. A scale linked by Bluetooth to an app on my iPad sat on the bathroom floor. With consistent use, these devices would provide a numeric picture of my general health and behaviors. They would give me intimate knowledge of my physical self, with all the information displayed neatly in graphs and charts. Not too many years ago, you had to go to medical specialists to get this kind of biological data. Now, whether your problem is migraines or mood swings, you can keep track of your ailment with a consumer device that costs around US $100. As these healthand-wellness gadgets proliferate, a “quantified self” movement is gaining strength: It's attracting athletes, fitness buffs, data lovers, hypochondriacs, and people just trying to lose some weight.

XML-based approaches for the integration of heterogeneous bio-molecular data

BackgroundThe today's public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing.ResultsIn this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML.ConclusionXML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources.

Natural computing methods in bioinformatics: A survey

Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multisource information, where one must integrate different kinds of biological data. Natural computing provides several possibilities in Bioinformatics, especially by presenting interesting nature-inspired methodologies for handling such complex problems. In this article we survey the role of natural computing in the domains of protein structure prediction, microarray data analysis and gene regulatory network generation. We utilize the learning ability of neural networks for adapting, uncertainty handling capacity of fuzzy sets and rough sets for modeling ambiguity, and the search potential of genetic algorithms for efficiently traversing large search spaces.

GPM: A Graph Pattern Matching Kernel with Diffusion for Chemical Compound Classification.

Classifying chemical compounds is an active topic in drug design and other cheminformatics applications. Graphs are general tools for organizing information from heterogenous sources and have been applied in modelling many kinds of biological data. With the fast accumulation of chemical structure data, building highly accurate predictive models for chemical graphs emerges as a new challenge.In this paper, we demonstrate a novel technique called Graph Pattern Matching kernel (GPM). Our idea is to leverage existing frequent pattern discovery methods and explore their application to kernel classifiers (e.g. support vector machine) for graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the database and use a diffusion process to label nodes in the graphs. Finally the kernel is computed using a set matching algorithm. We performed experiments on 16 chemical structure data sets and have compared our methods to other major graph kernels. The experimental results demonstrate excellent performance of our method.

Reproducible Statistical Analysis in Microarray Profiling Studies

Microarrays are a recent biotechnology that offers the hope of improved cancer classification. A number of publications presented clinically promising results by combining this new kind of biological data with specifically designed algorithmic approaches. But, reproducing published results in this domain is harder than it may seem. This paper presents examples, discusses the problems hidden in the published analyses and demonstrates a strategy to improve the situation which is based on the vignette technology available from the R and Bioconductor projects. The tool of a compendium is discussed to achieve reproducible calculations and to offer an extensible computational framework. A compendium is a document that bundles primary data, processing methods (computational code), derived data, and statistical output with textual documentation and conclusions. It is interactive in the sense that it allows for the modification of the processing options, plugging in new data, or inserting further algorithms and visualizations. Due to the complexity of the algorithms, the size of the data sets, and the limitations of the medium printed paper it is usually not possible to report all the minutiae of the data processing and statistical computations. The technique of a compendium allows a complete critical assessment of a complex analysis.

A Description Scheme of Biological Processes Based on Elementary Bricks of Action

With the fast growth of high-throughput strategies in Biology, there is a strong need to accelerate knowledge acquisition and organization of molecular functions. Unfortunately, although we know that there is a correlation between protein molecules and their functions, we are unable to clearly identify this link. Here, we revisit the current views of protein functions as well as their annotation, and we show that they are incompatible with unambiguous interpretations and the use of this knowledge. We describe herein a description scheme for biological processes based on elementary bricks of action that may be associated with biological molecules. To retrieve the descriptive quality found in annotations of other kinds of biological data, it was decided to develop a scheme involving four levels of abstraction: Basic Elements of Action, Biological Activities, Biological Functionalities and Biological Roles. This multi-level organization is a generic method; it allows for a description of biological processes by using a limited number of elementary bricks of action. Moreover, by using this description of biological processes, it should now be possible to clearly identify unambiguous relationships between the organization of biological processes and the structural or functional organizations of biological molecules.

GIMS: an integrated data storage and analysis environment for genomic and functional data.

Effective analyses in functional genomics require access to many kinds of biological data. For example, the analysis of upregulated genes in a microarray experiment might be aided by information concerning protein interactions or proteins' cellular locations. However, such information is often stored in different formats at different sites, in ways that may not be amenable to integrated analysis. The Genome Information Management System (GIMS) is an object database that integrates genomic data with data on the transcriptome, protein-protein interactions, metabolic pathways and annotations, such as gene ontology terms and identifiers. The resulting system supports the running of analyses over this integrated data resource, and provides comprehensive facilities for handling and interrelating the results of these analyses. GIMS has been used to store Saccharomyces cerevisiae data, and we demonstrate how the integrated storage of diverse types of data can be beneficial for analysis, using combinations of complex queries. As an example, we describe how GIMS has been used to analyse a collection of aryl alcohol dehydrogenase gene deletion mutants. The GIMS database can be accessed remotely using a Java application that can be downloaded from http://img.cs.man.ac.uk/gims.

Bringing sequences to life: how bioinformatics corporealizes sequence data

Sequence databases have become more visible through the heavy publicity associated with the Human Genome Project. This paper looks at some of the emerging artefacts and systems developed to read, write, order and visualise sequence data and other kinds of biological data retrieved from databases and databanks such as GenBank. It argues that bioinformatics software can be regarded as a symptom of a broad and powerful transformation in biological knowledges and in the biopolitical constitution of living bodies. Two facets of this transformation are emphasised. Firstly, examining bioinformatics software might help us situate how sequence data is actually circulating, and to what ends. In particular, the paper looks at the significance of sequence comparison and protein folding problems. Secondly, an emerging nexus of property relations and intellectual work can be detected within the ordering of sequence data carried out bioinformatics.

Quantitative drug design studies. II. Development and application of new electronic substituent parameters.

New electronic substituent parameters for quantitative drug design were developed from the standpoint of the frontier orbital theory using the extended Hückel method. Each substituent was characterized by three constants (E, Re, I), which were derived from the SOMO (solely occupied molecular orbital) energy and the coefficient of the SOMO hybrid orbital responsible for bonding to the parent moiety. These correspond to the SOMO-SOMO energy difference (E), the resistivity of the path (Re), and the flow intensity (I) respectively, when the frontier electron flow occurs between the standard parent and the substituent. They can be regarded as major factors governing the electronic inductive-field effect of the substituent. As the result of analyses of various kinds of biological data, they were found to be excellent and widely applicable parameters to the structure-activity problems. In this paper, the new frontier substituent constant values for 150 kinds of substituents and 58 kinds of equations to which the new constants were successfully applied are presented.

A program for fitting of hypnograms and other biological data by orthogonal polynomials

Many biological phenomenon are nonlinear and poorly approximated by linear regression. The program POLFIT calculates curvilinear regressions by fitting of orthogonal polynomials. This program, as well as two supporting programs (CORREC: disk storage, and CALCML: calculation of cumulated values of series of observations), was primarily designed for the study of the temporal organization of sleep components. They can be used as well for any other kind of biological data.