History and Future of KALIS: Towards Computer-assisted Decision Making in Prescriptive Medicine

Friedrichs M, Shoshi A. History and Future of KALIS: Towards Computer-assisted Decision Making in Prescriptive Medicine. Journal of Integrative Bioinformatics. 2019;16(3): 20190011.With an increasing older population in Germany and the need for polypharmacy to treat multimorbid patients computer-assisted decision making on an individual level is increasingly important to reduce prescription errors and adverse drug reactions. While current systems focus on guidelines and prescribing information, molecular information is equally important for explanation and discovery of drug-related problems. Based on the existing KALIS system and newer projects like PIMBase, a new concept for the KALIS-2 system is presented. Improvements to the modularisation of components enable future extension and greater maintainability. Interoperability with available electronic health records standards and protocols allows the integration and communication with existing workflows for healthcare professionals. Finally, new visualisation modes empower the user to explore and analyze the patient situation in an individual patient subgraph. For offline use and dialogue between patient and general practitioner, the results can be printed out using a new reporting tool. The adherence to findings from previous decision support systems and reasons for their failed adoption is an important task in the development of KALIS-2

Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects

Zolotareva O, Saik OV, Königs C, et al. Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects. Scientific Reports. 2019;9(1): 16302.Asthma and hypertension are complex diseases coinciding more frequently than expected by chance. Unraveling the mechanisms of comorbidity of asthma and hypertension is necessary for choosing the most appropriate treatment plan for patients with this comorbidity. Since both diseases have a strong genetic component in this article we aimed to find and study genes simultaneously associated with asthma and hypertension. We identified 330 shared genes and found that they form six modules on the interaction network. A strong overlap between genes associated with asthma and hypertension was found on the level of eQTL regulated genes and between targets of drugs relevant for asthma and hypertension. This suggests that the phenomenon of comorbidity of asthma and hypertension may be explained by altered genetic regulation or result from drug side effects. In this work we also demonstrate that not only drug indications but also contraindications provide an important source of molecular evidence helpful to uncover disease mechanisms. These findings give a clue to the possible mechanisms of comorbidity and highlight the direction for future research

Graafitietokantojen sovelluksia: systemaattinen kirjallisuuskatsaus

Tässä työssä kartoitetaan akateemisessa tutkimuksessa esiintyviä graafitietokantoja, niiden sovellusaloja sekä niihin liitettyjä hyötyjä ja haittoja. Tutkimusmenetelmänä on systemaattinen kirjallisuuskatsaus, jossa tunnistettiin 111 kriteerit täyttävää artikkelia vuosilta 2017–2021. Artikkeleja analysoitiin sisällönanalyysin keinoin. Graafitietokantojen sovellusaloja tunnistettiin 25. Sovellusaloilla tieto on tyypillisesti mallinnettavissa kompleksisina verkkoina. Yleisimpiä aloja olivat bioinformatiikka, sosiaaliset verkostot, tietoverkot ja geografinen tieto. Yksittäisistä graafitietokannoista ylivoimaisesti käytetyin oli Neo4j: se oli käytössä valtaosassa artikkelien sovelluksista. Muut graafitietokannat olivat edustettuna vähäisessä määrin aineistossa. Graafitietokantojen käytölle tunnistettiin kymmenen hyötyä. Yleisimmin mainitut hyödyt olivat graafikyselyiden ja algoritmien hyödyntäminen sekä graafitietokantojen soveltuvuus verkottuneelle datalle. Näiden jälkeen yleisimpinä hyötyinä tulivat selitysvoima erilaisissa analyyseissa, suorituskyky, visualisointiominaisuudet, tietokantakaavion joustavuus ja graafitietomallin ymmärrettävyys. Eri haittoja puolestaan tunnistettiin yhdeksän: haittoja mainittiin kuitenkin ylipäänsä huomattavasti hyötyjä harvemmin. Yleisimmin mainitut haitat olivat suorituskyky ja graafitietokantojen opettelu: molemmat oli mainittu kohtalaisen usein myös hyötynä. Tätä voi selittää sillä, että graafitietokantojen suorituskyvyssä on eroja eri sovellusten välillä: graafitietokantojen ja -kyselykielten koettu vaikeustaso taas riippuu tutkijoiden näkemyksistä. Lisäksi harvemmin mainittuja haittoja olivat muun muassa graafitietokantojen soveltumattomuus tietynlaiselle datalle ja alempi kypsyysaste verrattuna relaatiotietokantoihin

GenCoNet - A Graph Database for the Analysis of Comorbidities by Gene Networks

Shoshi A, Hofestädt R, Zolotareva O, et al. GenCoNet - A Graph Database for the Analysis of Comorbidities by Gene Networks. JOURNAL OF INTEGRATIVE BIOINFORMATICS. 2018;15(4): 20180049.The prevalence of comorbid diseases poses a major health issue for millions of people worldwide and an enormous socio-economic burden for society. The molecular mechanisms for the development of comorbidities need to be investigated. For this purpose, a workflow system was developed to aggregate data on biomedical entities from heterogeneous data sources. The process of integrating and merging all data sources of the workflow system was implemented as a semi-automatic pipeline that provides the import, fusion, and analysis of the highly connected biomedical data in a Neo4j database GenCoNet. As a starting point, data on the common comorbid diseases essential hypertension and bronchial asthma was integrated. GenCoNet (https: / /genconet.kalis-amts.de) is a curated database that provides a better understanding of hereditary bases of comorbidities

GenCoNet – A Graph Database for the Analysis of Comorbidities by Gene Networks

The prevalence of comorbid diseases poses a major health issue for millions of people worldwide and an enormous socio-economic burden for society. The molecular mechanisms for the development of comorbidities need to be investigated. For this purpose, a workflow system was developed to aggregate data on biomedical entities from heterogeneous data sources. The process of integrating and merging all data sources of the workflow system was implemented as a semi-automatic pipeline that provides the import, fusion, and analysis of the highly connected biomedical data in a Neo4j database GenCoNet. As a starting point, data on the common comorbid diseases essential hypertension and bronchial asthma was integrated. GenCoNet (https://genconet.kalis-amts.de) is a curated database that provides a better understanding of hereditary bases of comorbidities