A knowledge-driven approach for crystallographic protein model completion.
Ontology highlight
ABSTRACT:
SUBMITTER: Joosten K
PROVIDER: S-EPMC2467521 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging. We extend these models to the hyperbolic space so as to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model, dubbed HyperKG, can capture and show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models and effectively represent certain types of rules. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-49461-2_12) contains supplementary material, which is available to authorized users.
| S-EPMC7250606 | biostudies-literature
Project description:BackgroundExisting resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR).MethodsWe first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet?+?and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs.ResultsWe used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet?+?graph with eRAM and another clinical diagnostic tool, the Phenomizer.ConclusionsPer our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis.
| S-EPMC6376651 | biostudies-literature
Project description:The extent to which the librations of rigid molecules can model the crystallographic temperature factor profiles of proteins has been examined. For all proteins considered, including influenza virus hemagglutinin, glutathione reductase, myohemerythrin, myoglobin, and streptavidin, a simple 10-parameter model [V. Schomaker and K. N. Trueblood (1968) Acta Crystallogr. Sect. B 24, 63-76] is found to reproduce qualitatively the patterns of maxima and minima in the isotropic backbone meansquare displacements. Large deviations between the rigid molecule and individual atomic temperature factors are found to be correlated with a region in hemagglutinin for which the refined structural model is unsatisfactory and with errors in the structure in a partially incorrect model of myohemerythrin. For the high-resolution glutathione reductase structure, better results are obtained on treating each of the compact domains in the structure as independent rigid bodies. The method allows for the refinement of reliable temperature factors with the introduction of minimal parameters and may prove useful for the evaluation of models in the early stages of x-ray structure refinement. While these results by themselves do not establish the nature of the underlying displacements, the success of the rigid protein model in reproducing qualitative features of temperature factor profiles suggests that rigid body refinement results should be considered in any interpretation of crystallographic thermal parameters.
| S-EPMC51321 | biostudies-other
Project description:ObjectiveTo discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.MethodsWe propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.ResultsAccuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.ConclusionWe showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.
| S-EPMC7869625 | biostudies-literature
Project description:BackgroundThe ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information.ResultsBiofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories.Via Biofilter 2.0 researchers can:• Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation• Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest• Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing.ConclusionsBiofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes.
| S-EPMC3917600 | biostudies-literature
Project description:Effective dialogue generation for task completion is challenging to build. The task requires the response generation system to generate the responses consistent with intent and slot values, have diversity in response and be able to handle multiple domains. The response also needs to be context relevant with respect to the previous utterances in the conversation. In this paper, we build six different models containing Bi-directional Long Short Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT) based encoders. To effectively generate the correct slot values, we implement a copy mechanism at the decoder side. To capture the conversation context and the current state of the conversation we introduce a simple heuristic to build a conversational knowledge graph. Using this novel algorithm we are able to capture important aspects in a conversation. This conversational knowledge-graph is then used by our response generation model to generate more relevant and consistent responses. Using this knowledge-graph we do not need the entire utterance history, rather only the last utterance to capture the conversational context. We conduct experiments showing the effectiveness of the knowledge-graph in capturing the context and generating good response. We compare these results against hierarchical-encoder-decoder models and show that the use of triples from the conversational knowledge-graph is an effective method to capture context and the user requirement. Using this knowledge-graph we show an average performance gain of 0.75 BLEU score across different models. Similar results also hold true across different manual evaluation metrics.
| S-EPMC9910720 | biostudies-literature
Project description:A simple and very efficient protein design strategy is proposed by developing some recently introduced theoretical tools which have been successfully applied to exactly solvable protein models. The design approach is implemented by using three amino acid classes and it is based on the minimization of an appropriate energy function. For a given native state the results of the design procedure are compared, through a statistical analysis, with the properties of an ensemble of sequences folding in the same conformation. If the success rate is computed on those sites designed with high confidence, it can be as high as 80%. The method is also able to identify key sites for the folding process: results for 2ci2 and barnase are in very good agreement with experimental results.
| S-EPMC1301249 | biostudies-other
Project description:Telomere repeat DNA forms a nucleo-protein structure that can obstruct chromosomal DNA replication, especially under conditions of replication stress. Transcription of telomere repeats can initiate at subtelomeric CTCF-binding sites to generate telomere repeat-encoding RNA (TERRA), but the role of transcription, CTCF, and TERRA in telomere replication is not known. Here, we have used CRISPR/Cas9 gene editing to mutate CTCF-binding sites at the putative start site of TERRA transcripts for a class of subtelomeres. Under replication stress, telomeres lacking CTCF-driven TERRA exhibit sister-telomere loss and upon entry into mitosis, exhibit the formation of ultra-fine anaphase bridges and micronuclei. Importantly, these phenotypes could be rescued by the forced transcription of TERRA independent of CTCF binding. Our findings indicate that subtelomeric CTCF facilitates telomeric DNA replication by promoting TERRA transcription. Our findings also demonstrate that CTCF-driven TERRA transcription acts in cis to facilitate telomere repeat replication and chromosome stability.
| S-EPMC5727389 | biostudies-literature
Project description:Xenografts--as simplified animal models of cancer-differ substantially in vasculature and stromal architecture when compared to clinical tumours. This makes mathematical model-based predictions of clinical outcome challenging. Our objective is to further understand differences in tumour progression and physiology between animal models and the clinic. To achieve that, we propose a mathematical model based upon tumour pathophysiology, where oxygen--as a surrogate for endocrine delivery--is our main focus. The Oxygen-Driven Model (ODM), using oxygen diffusion equations, describes tumour growth, hypoxia and necrosis. The ODM describes two key physiological parameters. Apparent oxygen uptake rate (k'R) represents the amount of oxygen cells seem to need to proliferate. The more oxygen they appear to need, the more the oxygen transport. k'R gathers variability from the vasculature, stroma and tumour morphology. Proliferating rate (kp) deals with cell line specific factors to promote growth. The KH,KN describe the switch of hypoxia and necrosis. Retrospectively, using archived data, we looked at longitudinal tumour volume datasets for 38 xenografted cell lines and 5 patient-derived xenograft-like models. Exploration of the parameter space allows us to distinguish 2 groups of parameters. Group 1 of cell lines shows a spread in values of k'R and lower kp, indicating that tumours are poorly perfused and slow growing. Group 2 share the value of the oxygen uptake rate (k'R) and vary greatly in kp, which we interpret as having similar oxygen transport, but more tumour intrinsic variability in growth. However, the ODM has some limitations when tested in explant-like animal models, whose complex tumour-stromal morphology may not be captured in the current version of the model. Incorporation of stroma in the ODM will help explain these discrepancies. We have provided an example. The ODM is a very simple -and versatile- model suitable for the design of preclinical experiments, which can be modified and enhanced whilst maintaining confidence in its predictions.
| S-EPMC4627780 | biostudies-literature
Project description:The safety and efficacy of the transoral endoscopic thyroidectomy vestibular approach (TOETVA) continues to be verified with the growing literature in regards to the procedure. While early cases were thyroid lobectomies performed for benign disease, the indications for TOETVA have now expanded to include total thyroidectomy for select small well-differentiated thyroid cancers (DTCs). Oncologic efficacy of this procedure remains unproven at this time, as the procedure was described only recently. Furthermore, as many of the cases completed via TOETVA are often diagnostic lobectomies, the appropriate management for patients requiring or opting for further surgical intervention per American Thyroid Association (ATA) guidelines has not been established. Here we present a case of a diagnostic lobectomy via TOETVA followed by interval completion thyroidectomy via the same approach for minimally invasive Hurthle cell carcinoma. Postoperative ultrasound demonstrated no evidence of thyroid remnant and serum thyroglobulin without circulating anti-thyroid antibodies was 0.3 ng/mL (reference range, 1.5-38.5 ng/mL) following the patient's completion thyroidectomy.
| S-EPMC6107595 | biostudies-literature