SEDIC Project - Semantic medicine
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This document discusses a project called Semantic Medicine that aims to develop an ontology and application for herbal medicine. It will extract data on medicinal plants from existing sources and ontologies, and make it accessible via a REST API and SPARQL endpoint. The application will allow users to search for plant remedies based on medical conditions or search for plant characteristics. It outlines plans to develop an ontology for herbal medicine, REST endpoints for retrieving plant and remedy data, and priorities to focus on modeling and the API before graphical aspects.
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Online Resources to Support Open Drug Discovery Systems
Online Resources to Support Open Drug Discovery SystemsUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The document discusses online resources that can support open drug discovery systems. It outlines how pharmaceutical companies spend billions annually on R&D and how public domain data from sources like literature, patents and databases could provide high value. However, such data is difficult to integrate and navigate due to a lack of standards and interoperability between sources. The Open PHACTS project aims to address this by developing standards to semantically integrate drug discovery data from public and private sources.Medicine as data science
This document discusses biomedical informatics and the increasing role of data in medicine. It notes that medicine is becoming a more data-intensive field due to growing sources of electronic health data. Biomedical data is often large in volume, diverse, complex, weakly structured, noisy, and inconsistent. Extracting knowledge from this "big data" through techniques like data mining, machine learning, and integrating human-computer interaction can provide insights to improve healthcare outcomes. Key applications include personalized and predictive medicine through patient stratification and risk analysis. However, overcoming obstacles like heterogeneous and non-standardized data is challenging.
Medicine as a data science
This document discusses biomedical informatics and the use of big data in medicine. It makes three key points:
1) Medicine is increasingly becoming a data-intensive field as more patient data is digitized and generated through technologies like electronic health records, research databases, clinical trials, and remote patient monitoring. This data is large in volume, diverse, complex, and noisy.
2) Effective analysis of this "big data" through techniques like data mining, machine learning, and knowledge discovery can provide insights to improve patient outcomes, accelerate research, and enable precision and predictive medicine. However, heterogeneous and inconsistent data presents challenges.
3) The human element is important in making sense of data and applying knowledge.
2011-11-28 Open PHACTS at RSC CICAG
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
Explore new Frontiers in Medicine with AI.pdf
Welcome to the future of drug discovery and development! Artificial Intelligence is transforming the landscape of pharmaceutical research, particularly in drug repurposing. This video guides you through the ways Artificial Intelligence is revolutionizing the process and contributing to the discovery of new therapeutic applications for existing drugs.
Explore new Frontiers in Medicine with AI (2).pdf
Welcome to the future of drug discovery and development! Artificial Intelligence (AI) is transforming the landscape of pharmaceutical research, particularly in drug repurposing. This brochure guides you through the ways AI is revolutionizing the process and contributing to the discovery of new therapeutic applications for existing drugs.
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This document summarizes a systematic review of concepts related to health literacy in online information environments. The review examined 71 peer-reviewed articles published between 2011-2016. Most articles were from medical and health sciences. The review found that the most commonly used definitions of health literacy and eHealth literacy focus on an individual's ability to understand and utilize health information to make appropriate health decisions. While the definitions are similar in this regard, they differ in how they address factors like prior knowledge and critical evaluation of information. The review discusses opportunities to further examine and operationalize the concepts in different contexts and how the online environment is considered.Visual Analytics for Healthcare - Panel at AMIA 2012 in Chicago
AMIA 2012 Panel on Visual Analytics for Healthcare
Organizer:
Adam Perer, PhD
Research Scientist
IBM T.J. Watson Research Center, Hawthorne, NY
Panelists:
Ben Shneiderman, PhD
Professor, Computer Science
University of Maryland, College Park, MD
Yuval Shahar, PhD
Professor, Head of the Medical Informatics Research Center
Ben Gurion University, Beer Sheva, Israel
Jeffrey Heer, PhD
Assistant Professor, Computer Science
Stanford University, Stanford, CA
David Gotz, PhD
Research Scientist
IBM T.J. Watson Research Center, Hawthorne, NY
Abstract
With the proliferation of medical information technology, users at all levels of the healthcare system have access to more data than ever before6. This data can be of tremendous value but is often difficult to access and interpret. For example clinicians are often faced with the challenging task of analyzing large amounts of unstructured, multi-modal, and longitudinal data to effectively diagnose and monitor the progression of a patient’s disease4,5. Similarly, patients are confronted with the difficult task of understanding the trends and correlations within data related to their own health. At the institutional level, healthcare organizations are faced with the desire to use data to improve overall operational efficiency and performance, while continuing to maintain the quality of patient care and safety.
Recent advances in visualization and visual analytics have the potential to help each of the user groups listed above do more with the often overwhelming amount of data available to them 1,3,7,8. However, to be successful, visualization designers and clinicians must work together closely to ensure that the right technologies are used to help address the meaningful problems. Unfortunately, despite the continuous use of scientific visualization and visual analytics in medical applications, the lack of communication between engineers and physicians has meant that only basic visualization and analytics techniques are currently employed in clinical practice2,9.
The goal of this panel is to present state-of-the-art visualization applications for healthcare and engage the leading physicians and clinical researchers at AMIA to discuss the areas in healthcare where additional visualization techniques are most needed.
Mrr 05152011 pmdb_proposal
The document outlines the Pharmacomicrobiomics Database (PMDB) project, which aims to catalog interactions between drugs and the human microbiome. It discusses how the Human Genome Project and Human Microbiome Project motivated the new field of pharmacomicrobiomics. The PMDB will be a MySQL database containing information on drugs, microbial strains, body sites, pathways, and publications. So far, the team has developed a proposed database structure and website, published two papers on pharmacomicrobiomics, and recruited students to help curate data.
2009 09 Lod London
The document discusses Linked Open Drug Data (LODD), a project linking drug and biomedical datasets as linked open data. It describes several datasets that have been converted to RDF using tools like D2R Server, totaling over 8 million triples. Challenges discussed include understanding source data semantics and creating links between heterogeneous datasets at scale. Applications mentioned include searching for clinical trials, side effects, and alternative medicines.
TLA poster
This document summarizes a scoping literature review on image-based information systems designed for and used by dermatologists. The review identified four key concepts in system development: users (which include dermatologists, other medical professionals, patients, and the general public), system types (proprietary LAN systems, secure PACS, EHRs, teledermatology, and websites), system purposes (diagnosis, training, collaboration, consumer health info), and access/security concerns (interoperability, privacy, regulations). The results suggest implications for developers and professionals to incorporate user experience testing prior to designing new systems.
Mrr iti phar_mu
This document summarizes a graduation project that involved creating a database called PharMu to explore relationships between resident microbes, drugs, and human body sites. The project involved mining existing literature to extract known microbe-drug interactions and curating the data to create a relational database. Functionalities included search capabilities and an admin backend. Future work includes deployment on a public server and integrating additional data sources and classification hierarchies.
INBIOMEDvision Workshop at MIE 2011. Victoria López
1) Personalized medicine currently faces challenges in processing large-scale genomic data, interpreting the functional effects of genomic variations, integrating systems-level data, and translating discoveries into medical practice.
2) Bioinformatics can help address these challenges through algorithms for mapping and aligning sequencing data, predicting functional effects, prioritizing genes, integrating multi-omics data into networks, and disseminating discoveries through databases to inform medical practice.
3) Fully realizing personalized medicine will require overcoming limitations of current approaches, validating computational predictions, and updating medical practice and education to routinely incorporate genomic information.
AETIONOMY Overview AD/PD Conference 2015 Nice
The document outlines the mission and approach of AETIONOMY, a project aimed at generating a mechanism-based taxonomy for Alzheimer's and Parkinson's diseases. The project seeks to increase knowledge of the causes of these diseases by curating publicly available data, developing disease models and ontologies, and identifying testable hypotheses on disease mechanisms. These hypotheses will then be validated in a prospective clinical study to identify patient subgroups. The ultimate goal is to lay the foundation for improved disease classification and targeted treatment approaches.
Thesis Defence 05-26-2016
This thesis presents the development of computational methods and tools using as input three-dimensional structures data of protein-ligand complexes. The tools are useful to mine, profile and predict data from protein-ligand complexes to improve the modeling and the understanding of the protein-ligand recognition. This thesis is divided into five sub-projects. In addition, unpublished results about positioning water molecules in binding pockets are also presented. I developed a statistical model, PockDrug, which combines three properties (hydrophobicity, geometry and aromaticity) to predict the druggability of protein pockets, with results that are not dependent on the pocket estimation methods. The performance of pockets estimated on apo or holo proteins is better than that previously reported in the literature (Publication I). PockDrug is made available through a web server, PockDrug-Server (http://pockdrug.rpbs.univ-paris-diderot.fr), which additionally includes many tools for protein pocket analysis and characterization (Publication II). I developed a customizable computational workflow based on the superimposition of homologous proteins to mine the structural replacements of functional groups in the Protein Data Bank (PDB). Applied to phosphate groups, we identified a surprisingly high number of phosphate non-polar replacements as well as some mechanisms allowing positively charged replacements. In addition, we observed that ligands adopted a U-shape conformation at nucleotide binding pockets across phylogenetically unrelated proteins (Publication III). I investigated the prevalence of salt bridges at protein-ligand complexes in the PDB for five basic functional groups. The prevalence ranges from around 70% for guanidinium to 16% for tertiary ammonium cations, in this latter case appearing to be connected to a smaller volume available for interacting groups. In the absence of strong carboxylate-mediated salt bridges, the environment around the basic functional groups studied appeared enriched in functional groups with acidic properties such as hydroxyl, phenol groups or water molecules (Publication IV). I developed a tool that allows the analysis of binding poses obtained by docking. The tool compares a set of docked ligands to a reference bound ligand (may be different molecule) and provides a graphic output that plots the shape overlap and a Jaccard score based on comparison of molecular interaction fingerprints. The tool was applied to analyse the docking poses of active ligands at the orexin-1 and orexin-2 receptors found as a result of a combined virtual and experimental screen (Publication V). The review of literature focusses on protein-ligand recognition, presenting different concepts and current challenges in drug discovery.
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- 1. Semantic Medicine Alexandra Lacatus, Emanuela Catana Faculty of Computer Science, Iasi
- 2. Motivation Herbal Medicine is an important alternative to drug-based medicine HM represents many people’s choices The web has plenty of relevant information (unstructured yet) The project combines the use of semantic data representation with useful information (medicinal plants) 2 Semantic Medicine, FCS Iasi, 2013
- 3. Current work Medicinal plant collections (HerbMed, MedlinePlus) Ontologies for modelling plant vocabularies (The Plant Ontology) Ontologies for indian medicinal plants (MP-Ontology, KIMP project) 3 Semantic Medicine, FCS Iasi, 2013
- 4. SEDIC Project Milestones HM-ontology specification Data extraction (websites, DBpedia) REST API + SPARQL endpoint to access the data Android client application 4 Semantic Medicine, FCS Iasi, 2013
- 5. Main user stories Search for a possible cure for an illness An illness is characterized by an affected body area and some affection parameters (intensity, type). The user can also specify a medical condition (contraindications, other diseases) Look up for the characteristics of a specific plant (search upon a plant name) 5 Semantic Medicine, FCS Iasi, 2013
- 6. System architecture 6 Semantic Medicine, FCS Iasi, 2013
- 7. Proposal – HM-Ontology A task-oriented ontology for representing medicinal plants 7 Semantic Medicine, FCS Iasi, 2013
- 8. Proposal – REST API GET /plantRemedy Gets the information about a single plant remedy POST /plantRemedy Uses the existing parameters to search for remedies GET /plant Returns information about a specific plant POST /plant Uses the parameters to search for plants with the specified name/location 8 Semantic Medicine, FCS Iasi, 2013
- 9. Until next time: priorities We will focus more on the modelling part and on the REST API implementation rather than on the esthetical aspects on this project Depending on the timeframe, we could include also extra features, like the possibility to add an upvote/downvote for a remedy 9 Semantic Medicine, FCS Iasi, 2013