The Biological Knowledge Gap

1971 ◽  
pp. 166-175
Author(s):  
J. J. BURNS
Keyword(s):  
Biological Knowledge ◽  
Knowledge Gap

Nuklearmedizin trifft Strahlentherapie

2010 ◽  
Vol 49 (S 01) ◽  
pp. S11-S15
Author(s):  
C. Schütze ◽  
M. Krause ◽  
A. Yaromina ◽  
D. Zips ◽  
M. Baumann
Keyword(s):  
Nuclear Medicine ◽  
Radiation Oncology ◽  
External Beam Radiotherapy ◽  
Biological Knowledge ◽  
Important Research ◽  
Tumour Control ◽  
Research Areas ◽  
Normal Tissue Damage ◽  
Local Tumour Control ◽  
Local Tumour

SummaryRadiobiological and cell biological knowledge is increasingly used to further improve local tumour control or to reduce normal tissue damage after radiotherapy. Important research areas are evolving which need to be addressed jointly by nuclear medicine and radiation oncology. For this differences of the biological distribution of diagnostic and therapeutic nuclides compared with the more homogenous dose-distribution of external beam radiotherapy have to be taken into consideration. Examples for interdisciplinary biology-based cancer research in radiation oncology and nuclear medicine include bioimaging of radiobiological parameters characterizing radioresistance, bioimage-guided adaptive radiotherapy, and the combination of radiotherapy with molecular targeted drugs.

Knowledge gap revisited

1994 ◽  
Author(s):  
Lee Susan Bilodeau
Keyword(s):  
Knowledge Gap

DMSP – Database for Modeling Signaling Pathways

2008 ◽  
Vol 47 (02) ◽  
pp. 104-148
Author(s):  
M. Breit ◽  
B. Pfeifer ◽  
C. Baumgartner ◽  
R. Modre-Osprian ◽  
B. Tilg ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Initial Step ◽  
Point Of View ◽  
Biological Knowledge ◽  
Literature Study ◽  
Online Databases ◽  
Qualitative Level ◽  
Pathway Models

Summary Objectives: Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. Methods: The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. Results: DMSP – Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNF? pathway model was implemented and tested using this approach. Conclusion: DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and systemtheoretic point of view.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

2020 ◽  
Vol 27 (35) ◽  
pp. 5856-5886 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Future Development ◽  
High Impact ◽  
Therapeutic Protein ◽  
Knowledge Gap ◽  
Therapeutic Activity ◽  
Drug Molecule ◽  
Protein Targets ◽  
Predictive Tools

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Ways that nurse practitioner students self-explain during diagnostic reasoning

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Leah Burt ◽  
Susan Corbridge ◽  
Colleen Corte ◽  
Laurie Quinn ◽  
Lorna Finnegan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Health Care Providers ◽  
Nurse Practitioner ◽  
Descriptive Analysis ◽  
Diagnostic Errors ◽  
Diagnostic Reasoning ◽  
Knowledge Structures ◽  
Future Research ◽  
Biological Knowledge ◽  
Care Providers

Abstract Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

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