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An introduction is presented in which the editor discusses several articles published within issue on DNA-centric modeling and practice for next-generation computing and communication systems.

It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person's B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome. [ABSTRACT FROM AUTHOR]

Paralytic ileus (PI) is the pseudo-obstruction of the intestine secondary to intestinal muscle paralysis. Causes of PI include electrolyte imbalances, gastroenteritis (inflammation or infection of the stomach or intestines), overuse of medications, abdominal surgery, etc. Predicting mortality in PI patients hospitalized to ICU is crucial for assessing the severity of illness and adjudicating the value of treatment strategy and resource planning. We have developed a Statistically Robust Machine Learning based Mortality Prediction framework namely SRML-MortalityPredictor that could potentially help intensivists, surgeons, and other medical professionals to carefully plan treatment strategies for critically ill PI patients. We used MIMIC III v1.4, a publicly available ICU database to extract patients data (with age > 18 years old) admitted to the ICU with paralytic ileus (PI) as their primary illness. At phase 1, the SRML-MortalityPredictor framework uses univariate statistical analysis to filter out those risk factors which are not associated with the label of the data. Subsequently, it uses the risk survival statistical methods such as cox-regression and Kaplan–Meier survival analyses. The cox-regression analysis provides the hazard ratio about the potential PI risk factors that later used in conjunction with the Kaplan–Meier analysis to generate a rank order list (highest to lowest risk factors). At phase 2, we used several machine learning classification approaches such as linear discriminant analysis (LDA), Gaussian naive bayes (GNB), decision tree (DT) model, k-nearest neighbor (KNN), and support vector machine (SVM) to find the one with highest predictive power using the rank order features extracted at phase 1. We have evaluated the SRML-MortalityPredictor framework and recorded the accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the curve (AUC) scores for each model. The SRML-MortalityPredictor framework with support vector machine (using RBF kernel) showed better performance and yielded an accuracy score: 81.30% and AUC score: 81.38% while predicting mortality in PI patients. We demonstrated a feasible framework for the mortality risk prediction in PI patients admitted to the ICU. The proposed framework could potentially be helpful for intensivists in clinical decision making. Further research is necessary to incorporate more risk factors associated with PI patients to ensure the adaptability of SRML-MortalityPredictor at the bedside. [ABSTRACT FROM AUTHOR]

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance. [ABSTRACT FROM AUTHOR]

Increased interest in the immune system's involvement in pathophysiological phenomena coupled with decreased DNA sequencing costs have led to an explosion of antibody and T cell receptor sequencing data collectively termed "adaptive immune receptor repertoire sequencing" (AIRR-seq or Rep-Seq). The AIRR Community has been actively working to standardize protocols, metadata, formats, APIs, and other guidelines to promote open and reproducible studies of the immune repertoire. In this paper, we describe the work of the AIRR Community's Data Representation Working Group to develop standardized data representations for storing and sharing annotated antibody and T cell receptor data. Our file format emphasizes ease-of-use, accessibility, scalability to large data sets, and a commitment to open and transparent science. It is composed of a tab-delimited format with a specific schema. Several popular repertoire analysis tools and data repositories already utilize this AIRR-seq data format. We hope that others will follow suit in the interest of promoting interoperable standards. [ABSTRACT FROM AUTHOR]

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.