AI in Bioinformatics

In bioinformatics science and computational molecular biology, artificial intelligence (AI) has rapidly gained interest. With the availability of numerous types of AI algorithms, it has become prevalent for researchers to use off-shelf programmes to identify their datasets and mine them. At present, researchers are facing difficulties in selecting the right approach that could be extended to a given data collection, with numerous intelligent approaches available in the literature. Researchers need instruments that present the data in an intuitive manner, annotated with meaning, precision estimates, and description. In the fields of bioinformatics and computational molecular biology (DNA sequencing), this article seeks to review the use of AI. These fields have evolved from the needs of biologists to use the large volumes of data continuously obtained in genomic science and to better understand them. For several approaches to bioinformatics and DNA sequencing, the fundamental impetus is the evolution of species and the difficulty of dealing with incorrect results. The type of software programmes developed by the scientific community to search, identify and mine numerous usable biological databases are also mentioned in this article, simulating biological experiments with and without mistakes. The review of antibody-antigen interactions and their diversity, and the study of epidemiological evidence that can help forecast antibody-antigen interactions and the induction of broadly neutralising antibodies are important questions to be answered in the field of vaccinology

Proteomics Discovery of Disease Biomarkers

Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points

Oncoproteomics: Opportunities, Challenges & Advanced Technologies

Oncoproteomics is nothing but the analysis of proteins and their interactions in a cancer cell through proteomic technologies. Oncoproteomics is playing a progressively significant part in diagnosis and the management of cancer. It also helps in the advancement of personalized therapy of cancer. Oncoproteomics holds great potential not only for opening the complicated molecular episodes of tumorigenesis but also for those that regulate clinically essential tumor habits, like metastasis, invasion, and resistance to treatment. Protein molecules show a significant impact on the evolution of cancer as it mainly develops due to abnormal signaling pathways. Detection and comprehension of these alterations is the major concept of oncoproteomics. Novel proteomic technologies related to cancer are defined in short, which are assisting not only in the comprehension of the mechanism of drug-resistant in cancer but also bestow some guides in management. For the diagnostic and prognostic categorization of the disease condition, and in measuring the drug efficiency and toxicity acclimatization of proteomic technologies in clinical laboratories is the fundamental objective of oncoproteomics. A considerable influence on the management of cancer patients and on a spectacular revolution in cancer research might notice by data obtained through such novel technologies. For the cancer therapy, the identification of novel targets, as well as an understanding of tumor development, might permit by the research of tumor-specific proteomic profiles. A wide perspective on drug-resistant and anticancer drug discovery, proteomic biomarkers and its function in the diagnosis of cancer, current innovation in proteomic technologies have tried to give in this review

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

Identification and validation of targets for cancer immunotherapy: from the bench-to-bedside

Chapter 12 in Novel Gene Therapy Approache

Review Article: Current Knowledge on Microarray Technology - An Overview

The completion of whole genome sequencing projects has led to a rapid increase in the availability of genetic information. In the field of transcriptomics, the emergence of microarray-based technologies and the design of DNA biochips allow high-throughput studies of RNA expression in cell and tissue at a given moment. It has emerged as one of the most important technology in the field of molecular biology and transcriptomics. Arrays of oligonucleotide or DNA sequences are being used for genome-wide genotyping and expression profiling, and several potential clinical applications have begun to emerge as our understanding of these techniques and the data they generate improves. From its emergence to date, several database, software and technology updates have been developed in the field of microarray technology. This paper reviews basics and updates of each microarray technology and serves to introduce newly compiled resources that will provide specialist information in this area.Keywords: Microarray, Databases, cDNA array, Oligonucleotide arra

Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation

BACKGROUND: Activation of naïve B lymphocytes by extracellular ligands, e.g. antigen, lipopolysaccharide (LPS) and CD40 ligand, induces a combination of common and ligand-specific phenotypic changes through complex signal transduction pathways. For example, although all three of these ligands induce proliferation, only stimulation through the B cell antigen receptor (BCR) induces apoptosis in resting splenic B cells. In order to define the common and unique biological responses to ligand stimulation, we compared the gene expression changes induced in normal primary B cells by a panel of ligands using cDNA microarrays and a statistical approach, CLASSIFI (Cluster Assignment for Biological Inference), which identifies significant co-clustering of genes with similar Gene Ontology™ annotation. RESULTS: CLASSIFI analysis revealed an overrepresentation of genes involved in ion and vesicle transport, including multiple components of the proton pump, in the BCR-specific gene cluster, suggesting that activation of antigen processing and presentation pathways is a major biological response to antigen receptor stimulation. Proton pump components that were not included in the initial microarray data set were also upregulated in response to BCR stimulation in follow up experiments. MHC Class II expression was found to be maintained specifically in response to BCR stimulation. Furthermore, ligand-specific internalization of the BCR, a first step in B cell antigen processing and presentation, was demonstrated. CONCLUSION: These observations provide experimental validation of the computational approach implemented in CLASSIFI, demonstrating that CLASSIFI-based gene expression cluster analysis is an effective data mining tool to identify biological processes that correlate with the experimental conditional variables. Furthermore, this analysis has identified at least thirty-eight candidate components of the B cell antigen processing and presentation pathway and sets the stage for future studies focused on a better understanding of the components involved in and unique to B cell antigen processing and presentation