Enhancing cyber assets visibility for effective attack surface management : Cyber Asset Attack Surface Management based on Knowledge Graph

The contemporary digital landscape is filled with challenges, chief among them being the management and security of cyber assets, including the ever-growing shadow IT. The evolving nature of the technology landscape has resulted in an expansive system of solutions, making it challenging to select and deploy compatible solutions in a structured manner. This thesis explores the critical role of Cyber Asset Attack Surface Management (CAASM) technologies in managing cyber attack surfaces, focusing on the open-source CAASM tool, Starbase, by JupiterOne. It starts by underlining the importance of comprehending the cyber assets that need defending. It acknowledges the Cyber Defense Matrix as a methodical and flexible approach to understanding and addressing cyber security challenges. A comprehensive analysis of market trends and business needs validated the necessity of asset security management tools as fundamental components in firms' security journeys. CAASM has been selected as a promising solution among various tools due to its capabilities, ease of use, and seamless integration with cloud environments using APIs, addressing shadow IT challenges. A practical use case involving the integration of Starbase with GitHub was developed to demonstrate the CAASM's usability and flexibility in managing cyber assets in organizations of varying sizes. The use case enhanced the knowledge graph's aesthetics and usability using Neo4j Desktop and Neo4j Bloom, making it accessible and insightful even for non-technical users. The thesis concludes with practical guidelines in the appendices and on GitHub for reproducing the use case

TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support

As the relevant literature and the number of experiments increase at a super linear rate, databases that curate and collect experimentally verified microRNA (miRNA) targets have gradually emerged. These databases attempt to provide efficient access to this wealth of experimental data, which is scattered in thousands of manuscripts. Aim of TarBase 6.0 (http://www.microrna.gr/tarbase) is to face this challenge by providing a significant increase of available miRNA targets derived from all contemporary experimental techniques (gene specific and high-throughput), while incorporating a powerful set of tools in a user-friendly interface. TarBase 6.0 hosts detailed information for each miRNA–gene interaction, ranging from miRNA- and gene-related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. All database entries are enriched with function-related data, as well as general information derived from external databases such as UniProt, Ensembl and RefSeq. DIANA microT miRNA target prediction scores and the relevant prediction details are available for each interaction. TarBase 6.0 hosts the largest collection of manually curated experimentally validated miRNA–gene interactions (more than 65 000 targets), presenting a 16.5–175-fold increase over other available manually curated databases

Requirements Management Tools: A Quantitative Assessment

This report is primarily aimed at people with some background in Requirements Engineering or practitioners wishing to assess tools available for managing requirements. We provide a starting point for this assessment, by presenting a brief survey of existing Requirements Management tools. As a part of the survey, we characterize a set of requirements management tools by outlining their features, capabilities and goals. The characterization offers a foundation to select and possibly customize a requirements engineering tool for a software project. This report consists of three parts. In Part I we define the terms requirements and requirements engineering and briefly point out the main components of the requirements engineering process. In Part II, we survey the characteristics and capabilities of 6 popular requirements management tools, available in the market. We enumerate the salient features of each of theses tools. In Part III, we briefly describe a Synergistic Environment for Requirement Generation. This environment captures additional tools augmenting the requirements generation process. A description of these tools is provided. In the concluding section, we present a discussion defining the ideal set of characteristics that should be embodied in a requirements management tool. This report is adapted from a compendium of assignments that were prepared by the students in a Requirements Engineering class offered in the Department of Computer Science at Virginia Tech

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks

Second Annual Conference on Astronomical Data Analysis Software and Systems. Abstracts

Abstracts from the conference are presented. The topics covered include the following: next generation software systems and languages; databases, catalogs, and archives; user interfaces/visualization; real-time data acquisition/scheduling; and IRAF/STSDAS/PROS status reports

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer’s disease: Identifying critical microRNA using machine learning

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer’s disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs’ potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease’s comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods

Computational Methods and Software Tools for Functional Analysis of miRNA Data

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.Junta de Andalucia PI-0173-2017 CV20.3672

Inferring RBP-mediated regulation in lung squamous cell carcinoma

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation of mRNAs. Dysregulations in RBP-mediated mechanisms have been found to be associated with many steps of cancer initiation and progression. Despite this, previous studies of gene expression in cancer have ignored the effect of RBPs. To this end, we developed a lasso regression model that predicts gene expression in cancer by incorporating RBP-mediated regulation as well as the effects of other well-studied factors such as copy-number variation, DNA methylation, TFs and miRNAs. As a case study, we applied our model to Lung squamous cell carcinoma (LUSC) data as we found that there are several RBPs differentially expressed in LUSC. Including RBP-mediated regulatory effects in addition to the other features significantly increased the Spearman rank correlation between predicted and measured expression of held-out genes. Using a feature selection procedure that accounts for the adaptive search employed by lasso regularization, we identified the candidate regulators in LUSC. Remarkably, several of these candidate regulators are RBPs. Furthermore, majority of the candidate regulators have been previously found to be associated with lung cancer. To investigate the mechanisms that are controlled by these regulators, we predicted their target gene sets based on our model. We validated the target gene sets by comparing against experimentally verified targets. Our results suggest that the future studies of gene expression in cancer must consider the effect of RBP-mediated regulation.No sponso