Integrated web visualizations for protein-protein interaction databases

BACKGROUND: Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. RESULTS: We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. CONCLUSIONS: Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing

Internalizing Dimensions Profiles of Children Referred for Externalizing Behaviours in School Psychological Services

Externalizing behaviours are among the most common and challenging childhood disorders. These behavioural traits are often associated with less obvious internal states, such as anxiety, depression or personality disorders, which are commonly ignored by mental health professionals. Recognizing and assessing these mood states in terms of anxiety level and depressive symptoms (especially self-esteem) and personality traits may help mental health specialist to design more effective interventions. 24 children aged 8 to 14 referred to school psychological services for disruptive behaviour disorders were compared to a control group, paired by age, gender and intellectual efficiency. Parents and teachers completed the Strenghs and Difficulties Questionnaire (SDQ) in order to assess disruptive behaviours. Manifest anxiety, depressive symptoms, personality traits were assessed using respectively the Revised Children's Manifest Anxiety Scale (R-CMAS), the Multiscore Depression Inventory for Children (MDI-C), and the Hierarchical Personality Inventory for Children (HiPIC). Children with externalizing disorders presented many discreet indices of affective distress as higher level of physiological anxiety, sad mood, instrumental helplessness, social introversion, pessimism and lower level of conscientiousness and benevolence. These results should encourage child and adolescent’s clinicians to screen for psychological vulnerabilities during a holistic psychological assessment including self-report questionnaires and child interviews in addition to the classical parents or teachers’ questionnaires

State-of-the-Art Explainability Methods with Focus on Visual Analytics Showcased by Glioma Classification

This study aims to reflect on a list of libraries providing decision support to AI models. The goal is to assist in finding suitable libraries that support visual explainability and interpretability of the output of their AI model. Especially in sensitive application areas, such as medicine, this is crucial for understanding the decision-making process and for a safe application. Therefore, we use a glioma classification model’s reasoning as an underlying case. We present a comparison of 11 identified Python libraries that provide an addition to the better known SHAP and LIME libraries for visualizing explainability. The libraries are selected based on certain attributes, such as being implemented in Python, supporting visual analysis, thorough documentation, and active maintenance. We showcase and compare four libraries for global interpretations (ELI5, Dalex, InterpretML, and SHAP) and three libraries for local interpretations (Lime, Dalex, and InterpretML). As use case, we process a combination of openly available data sets on glioma for the task of studying feature importance when classifying the grade II, III, and IV brain tumor subtypes glioblastoma multiforme (GBM), anaplastic astrocytoma (AASTR), and oligodendroglioma (ODG), out of 1276 samples and 252 attributes. The exemplified model confirms known variations and studying local explainability contributes to revealing less known variations as putative biomarkers. The full comparison spreadsheet and implementation examples can be found in the appendix

A multicentre, randomised, double-blind, placebo-controlled trial with the interleukin-1 receptor antagonist anakinra in patients with systemic-onset juvenile idiopathic arthritis (ANAJIS trial)

To assess the efficacy of the interleukin 1 receptor antagonist anakinra in systemic-onset juvenile idiopathic arthritis (SJIA).status: publishe

A historical perspective of biomedical explainable AI research

The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.</p

Targeting the chemokine receptor CXCR4 with histamine analog to reduce inflammation in juvenile arthritis

IntroductionAmong immune cells, activated monocytes play a detrimental role in chronic and viral-induced inflammatory pathologies, particularly in Juvenile Idiopathic Arthritis (JIA), a childhood rheumatoid arthritis (RA) disease. The uncontrolled activation of monocytes and excessive production of inflammatory factors contribute to the damage of bone-cartilage joints. Despite the moderate beneficial effect of current therapies and clinical trials, there is still a need for alternative strategies targeting monocytes to treat RA.MethodsTo explore such an alternative strategy, we investigated the effects of targeting the CXCR4 receptor using the histamine analog clobenpropit (CB). Monocytes were isolated from the blood and synovial fluids of JIA patients to assess CB's impact on their production of key inflammatory cytokines. Additionally, we administered daily intraperitoneal CB treatment to arthritic mice to evaluate its effects on circulating inflammatory cytokine levels, immune cell infiltrates, joints erosion, and bone resorption, as indicators of disease progression.ResultsOur findings demonstrated that CXCR4 targeting with CB significantly inhibited the spontaneous and induced-production of key inflammatory cytokines by monocytes isolated from JIA patients. Furthermore, CB treatment in a mouse model of collagen-induce arthritis resulted in a significant decrease in circulating inflammatory cytokine levels, immune cell infiltrates, joints erosion, and bone resorption, leading to a reduction in disease progression.DiscussionIn conclusion, targeting CXCR4 with the small amino compound CB shows promise as a therapeutic option for chronic and viral-induced inflammatory diseases, including RA. CB effectively regulated inflammatory cytokine production of monocytes, presenting a potential targeted approach with potential advantages over current therapies. These results warrant further research and clinical trials to explore the full therapeutic potential of targeting CXCR4 with CB-like molecules in the management of various inflammatory diseases