Content-based Representation and Retrieval of Visual Media: A State-of-the-Art Review

This paper reviews a number of recently available techniques incontent37420 of visual media and their application to the indexing,retrieval,92-36020 relevance assessment, interactive perception, annotation and re-use ofvisual-346 ments. 1. Background A few years ago, the problems of representation and retrieval ofvisual-276 were confined to specialized image databases (geographical, medical, pilotexperiments6 computerized slide libraries), in the professional applications of theaudiovisual20 -24 (production, broadcasting and archives), and in computerized training or education. Thepresent-23420 2 of multimedia technology and information highways has put content processing ofvisual-220 at the core of key application domains: digital and interactive video, large distributed digital libraries, multimedia publishing. Though the most important investments have been targeted at the information infrastructure (networks, servers, coding and compression,deliveryn, 356 multimedia systems arc..

Explainable Deep Learning AI: Methods and Challenges

International audienceExplainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence.The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented

PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus.

ObjectivesWe sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool.Materials and methodsWe generated Phenotype Clustering (PhenClust)-a novel application of semantic similarity for interpreting biological phenotype associations-using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool's application, and developed Docker containers with stable installations of two UMLS versions.ResultsPhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus.DiscussionClustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support.ConclusionPhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity

PathFXweb: a web application for identifying drug safety and efficacy phenotypes.

SUMMARY:Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug's pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb. AVAILABILITY AND IMPLEMENTATION:https://www.pathfxweb.net/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online

High Precision Prediction of Functional Sites in Protein Structures

<div><p>We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at <a href="http://feature.stanford.edu/wf4.0-beta" target="_blank">http://feature.stanford.edu/wf4.0-beta</a>.</p></div