scholarly journals RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Vidisha Singh ◽  
George D Kalliolias ◽  
Marek Ostaszewski ◽  
Maëva Veyssiere ◽  
Eleftherios Pilalis ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Knowledge Base ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
State Of The Art ◽  
Underlying Disease ◽  
Small Scale ◽  
Map Building ◽  
Scientific Publications ◽  
Molecular Map

Abstract Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

scholarly journals A Large-Scale Pseudoword-Based Evaluation Framework for State-of-the-Art Word Sense Disambiguation

2014 ◽  
Vol 40 (4) ◽  
pp. 837-881 ◽  
Author(s):  
Mohammad Taher Pilehvar ◽  
Roberto Navigli
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Evaluation Framework ◽  
Small Scale ◽  
Word Sense ◽  
Knowledge Based ◽  
Depth Analysis ◽  
Sense Disambiguation ◽  
The Impact

The evaluation of several tasks in lexical semantics is often limited by the lack of large amounts of manual annotations, not only for training purposes, but also for testing purposes. Word Sense Disambiguation (WSD) is a case in point, as hand-labeled datasets are particularly hard and time-consuming to create. Consequently, evaluations tend to be performed on a small scale, which does not allow for in-depth analysis of the factors that determine a systems' performance. In this paper we address this issue by means of a realistic simulation of large-scale evaluation for the WSD task. We do this by providing two main contributions: First, we put forward two novel approaches to the wide-coverage generation of semantically aware pseudowords (i.e., artificial words capable of modeling real polysemous words); second, we leverage the most suitable type of pseudoword to create large pseudosense-annotated corpora, which enable a large-scale experimental framework for the comparison of state-of-the-art supervised and knowledge-based algorithms. Using this framework, we study the impact of supervision and knowledge on the two major disambiguation paradigms and perform an in-depth analysis of the factors which affect their performance.

scholarly journals Drug2ways: Reasoning over causal paths in biological networks for drug discovery

2020 ◽  
Vol 16 (12) ◽  
pp. e1008464
Author(s):  
Daniel Rivas-Barragan ◽  
Sarah Mubeen ◽  
Francesc Guim Bernat ◽  
Martin Hofmann-Apitius ◽  
Daniel Domingo-Fernández
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Underlying Disease ◽  
Pharmacological Intervention ◽  
Drug Candidate ◽  
Drug Candidates ◽  
Advanced Therapies ◽  
The Impact

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

scholarly journals Drug2ways: Reasoning over causal paths in biological networks for drug discovery

2020 ◽  
Author(s):  
Daniel Rivas-Barragan ◽  
Sarah Mubeen ◽  
Francesc Guim Bernat ◽  
Martin Hofmann-Apitius ◽  
Daniel Domingo-Fernández
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Underlying Disease ◽  
Pharmacological Intervention ◽  
Drug Candidate ◽  
Drug Candidates ◽  
Advanced Therapies ◽  
The Impact

AbstractElucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model

2017 ◽  
Vol 4 (1) ◽  
pp. 100050 ◽  
Author(s):  
Vidisha Singh ◽  
Marek Ostaszewski ◽  
George D Kalliolias ◽  
Gilles Chiocchia ◽  
Robert Olaso ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Large Scale ◽  
Initial Conditions ◽  
Synovial Fibroblasts ◽  
Dynamical Model ◽  
Easy Access ◽  
Computational Systems Biology ◽  
Dynamical Study ◽  
Molecular Map ◽  
Interactive Map

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts’ advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts’ behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts’ activation.

Genetics of rheumatoid arthritis

2013 ◽  
Author(s):  
Steve Eyre ◽  
Jane Worthington
Keyword(s):  
Rheumatoid Arthritis ◽  
Large Scale ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Convincing Evidence ◽  
Susceptibility Locus ◽  
Small Scale ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hla Association

A range of epidemiological studies have clearly established that susceptibility to rheumatoid arthritis (RA) is determined by both genetic and environmental factors. Studies over the last five decades have used a variety of approaches to identify the genetic variants associated with disease. HLA DRB1 was the first RA susceptibility locus to be discovered and has the largest effect size. We describe current understanding of the complexities of HLA association for RA. Linkage and small-scale association studies prior to 2007 provided convincing evidence for only one more RA susceptibility locus, PTPN22. Major breakthroughs in high-throughput genotyping and systematic discovery and mapping of hundreds of thousands of single nucleotide polymorphisms (SNPs) led to large-scale genome-wide association studies used for the first time for RA in 2007. This approach has had a dramatic impact on our knowledge of the susceptibility loci for RA, such that over 60 risk variants have now been robustly identified. We present an overview of these studies and the loci that have been identified. We consider how this knowledge is contributing to a greater understanding of the aetiology and pathology of the disease and in turn how this can influence management of patients presenting with an inflammatory arthritis. We consider some of the unanswered questions and the approaches that will need to be taken to address them.

scholarly journals A glance at molecular mechanisms underlying cisplatin-induced nephrotoxicity and possible renoprotective strategies: a narrative review

2019 ◽  
Vol 28 (3) ◽  
pp. 292-9
Author(s):  
Bashar Adi Wahyu Pandhita ◽  
Deliana Nur Ihsani Rahmi ◽  
Nielda Kezia Sumbung ◽  
Bernardino Matthew Waworuntu ◽  
Regina Puspa Utami ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Defense Mechanism ◽  
Therapeutic Interventions ◽  
Cellular Damage ◽  
Small Scale ◽  
Renal Tubular Cells ◽  
Complex Process ◽  
Current Article ◽  
Renal Tubular

Cisplatin is a platinum-based drug that is usually used for the treatment of many carcinomas. However, it comes with several devastating side effects, including nephrotoxicity. Cisplatin toxicity is a very complex process, which is exacerbated by the accumulation of cisplatin in renal tubular cells via passive diffusion and transporter-mediated processes. Once cisplatin enters these cells, it induces the formation of reactive oxygen species that cause cellular damage, including DNA damage, inflammation, and eventually cell death. On a small scale, these damages can be mitigated by cellular antioxidant defense mechanism. However, on a large scale, such as in chemotherapy, this defense mechanism may fail, resulting in nephrotoxicity. The current article reviews the molecular mechanisms underlying cisplatin-induced nephrotoxicity and possible renoprotective strategies to determine novel therapeutic interventions for alleviating this toxicity.

scholarly journals Neural Machine Translation with Joint Representation

2020 ◽  
Vol 34 (05) ◽  
pp. 8285-8292
Author(s):  
Yanyang Li ◽  
Qiang Wang ◽  
Tong Xiao ◽  
Tongran Liu ◽  
Jingbo Zhu
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
Large Scale ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
The State ◽  
Small Scale ◽  
Neural Machine Translation ◽  
Joint Representation

Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e.g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency. In this paper, we employ Joint Representation that fully accounts for each possible interaction. We sidestep the inefficiency issue by refining representations with the proposed efficient attention operation. The resulting Reformer models offer a new Sequence-to-Sequence modelling paradigm besides the Encoder-Decoder framework and outperform the Transformer baseline in either the small scale IWSLT14 German-English, English-German and IWSLT15 Vietnamese-English or the large scale NIST12 Chinese-English translation tasks by about 1 BLEU point. We also propose a systematic model scaling approach, allowing the Reformer model to beat the state-of-the-art Transformer in IWSLT14 German-English and NIST12 Chinese-English with about 50% fewer parameters. The code is publicly available at https://github.com/lyy1994/reformer.

Genetics of rheumatoid arthritis

2013 ◽  
pp. 308-312
Author(s):  
Steve Eyre ◽  
Gisela Orozco ◽  
Jane Worthington
Keyword(s):  
Rheumatoid Arthritis ◽  
Large Scale ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Convincing Evidence ◽  
Susceptibility Locus ◽  
Small Scale ◽  
Nucleotide Polymorphisms ◽  
Risk Variants ◽  
Genetic And Environmental Factors

A range of epidemiological studies have clearly established that susceptibility to rheumatoid arthritis (RA) is determined by both genetic and environmental factors. Studies over the last five decades have used a variety of approaches to identify the genetic variants associated with disease. HLA-DRB1 was the first RA susceptibility locus to be discovered and has the largest effect size. We describe current understanding of the complexities of HLA association for RA. Linkage and small-scale association studies prior to 2007 provided convincing evidence for only one more RA susceptibility locus, PTPN22. Major breakthroughs in high-throughput genotyping, and systematic discovery and mapping of hundreds of thousands of single nucleotide polymorphisms (SNPs) led to large-scale genome-wide association studies used for the first time for RA in 2007. Widespread utilization of this approach has had a dramatic impact on our knowledge of the susceptibility loci for RA, such that over 100 risk variants have now been robustly identified. We present an overview of these studies and the loci that have been identified. We consider how this knowledge is contributing to a greater understanding of the aetiology and pathology of the disease, and in turn how this can influence management of patients presenting with an inflammatory arthritis. We consider some of the unanswered questions and the approaches that will need to be taken to address them.

Genetics of rheumatoid arthritis

2016 ◽  
Author(s):  
Steve Eyre ◽  
Jane Worthington ◽  
Sebastien Viatte
Keyword(s):  
Rheumatoid Arthritis ◽  
Large Scale ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Convincing Evidence ◽  
Susceptibility Locus ◽  
Small Scale ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hla Association

A range of epidemiological studies have clearly established that susceptibility to rheumatoid arthritis (RA) is determined by both genetic and environmental factors. Studies over the last five decades have used a variety of approaches to identify the genetic variants associated with disease. HLA DRB1 was the first RA susceptibility locus to be discovered and has the largest effect size. We describe current understanding of the complexities of HLA association for RA. Linkage and small-scale association studies prior to 2007 provided convincing evidence for only one more RA susceptibility locus, PTPN22. Major breakthroughs in high-throughput genotyping, and systematic discovery and mapping of hundreds of thousands of single nucleotide polymorphisms (SNPs) led to large-scale genome-wide association studies used for the first time for RA in 2007. Widespread utilization of this approach has had a dramatic impact on our knowledge of the susceptibility loci for RA, such that over 100 risk variants have now been robustly identified. We present an overview of these studies and the loci that have been identified. We consider how this knowledge is contributing to a greater understanding of the aetiology and pathology of the disease, and in turn how this can influence management of patients presenting with an inflammatory arthritis. We consider some of the unanswered questions and the approaches that will need to be taken to address them.

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