Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain

Paul, Razan; Groza, Tudor; Hunter, Jane; Zankl, Andreas

doi:10.1016/j.jbi.2013.12.001

Cited by 15 publications

(11 citation statements)

References 15 publications

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“…Paul et al combined rule mining and the Dempster-Shafer theory to calculate probabilistic associations between sets of clinical features and six skeletal dysplasia types recorded in the European Skeletal Dysplasia Network patient registry. Despite data sparseness, the mixed algorithm outperformed specialist medical diagnosis and five different ML methods [35]. For Mucopolysaccharidosis type II (MPS II, ORPHA:580) diagnosis prediction, a naïve Bayes classifier was developed and trained with literature data.…”

Section: Phenotype-driven Diagnosismentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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Section: Phenotype-driven Diagnosismentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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“…The application area of clinical research consists of a varied set of specific goals, which represent in practice coherent research streams on their own. Phenotypes have been used as unique source of data, for example, for disease prediction [ 72 , 73 ], mining key disease characteristics or characteristic phenotypes [ 16 , 17 , 74 ] or patient match-making [ 51 ]. Furthermore, phenotypes have been used to support the understanding of the genetic mechanism of diseases via direct association with genotype data [ 5 , 9 , 12 ], as a mapping bridge across species data [ 7 , 8 ] and in conjunction with the entire set of OMICS data [ 75 ] or to improve variant prioritization for accurate diagnosis [ 76 , 77 ].…”

Section: State-of-the-art Phenome Researchmentioning

confidence: 99%

The digital revolution in phenotyping

Oellrich¹,

Collier²,

Groza³

et al. 2015

Brief Bioinform

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Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support ‘bench to bedside’ efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

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“…We then searched for topic associative rules that would best characterize articles from each cluster. Associative rule mining methods have been used in many different contexts, from the identification of consumer products frequently bought together to the investigation of similarities between gene sets in biology [2][3][4][5]. Here, we applied these methods to examine associative patterns between topics.…”

Section: Introductionmentioning

confidence: 99%

The recipes of Philosophy of Science: Characterizing the semantic structure of corpora by means of topic associative rules

Malaterre

Chartier²,

Lareau

2020

PLoS ONE

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Scientific articles have semantic contents that are usually quite specific to their disciplinary origins. To characterize such semantic contents, topic-modeling algorithms make it possible to identify topics that run throughout corpora. However, they remain limited when it comes to investigating the extent to which topics are jointly used together in specific documents and form particular associative patterns. Here, we propose to characterize such patterns through the identification of “topic associative rules” that describe how topics are associated within given sets of documents. As a case study, we use a corpus from a subfield of the humanities—the philosophy of science—consisting of the complete full-text content of one of its main journals: Philosophy of Science. On the basis of a pre-existing topic modeling, we develop a methodology with which we infer a set of 96 topic associative rules that characterize specific types of articles depending on how these articles combine topics in peculiar patterns. Such rules offer a finer-grained window onto the semantic content of the corpus and can be interpreted as “topical recipes” for distinct types of philosophy of science articles. Examining rule networks and rule predictive success for different article types, we find a positive correlation between topological features of rule networks (connectivity) and the reliability of rule predictions (as summarized by the F-measure). Topic associative rules thereby not only contribute to characterizing the semantic contents of corpora at a finer granularity than topic modeling, but may also help to classify documents or identify document types, for instance to improve natural language generation processes.

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Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain

Cited by 15 publications

References 15 publications

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

The digital revolution in phenotyping

The recipes of Philosophy of Science: Characterizing the semantic structure of corpora by means of topic associative rules

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