DECIMER 1.0: deep learning for chemical image recognition using transformers

Rajan, Kohulan; Zielesny, Achim; Steinbeck, Christoph

doi:10.1186/s13321-021-00538-8

Cited by 47 publications

(34 citation statements)

References 31 publications

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“…The network shows a few article that use methods in two cited articles, e.g. Kohulan Rajan, 2021 and Henning Otto Brinkhaus, 2022 [ 23 , 27 ]. Of course, if we do not limit the network to a subset of articles (here, Journal of Cheminformatics articles with CiTO annotation), the network becomes more interesting, but also much more complex.…”

Section: Annotated Citation Networkmentioning

confidence: 99%

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Two years of explicit CiTO annotations

Willighagen

2023

J Cheminform

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Citations are an essential aspect of research communication and have become the basis of many evaluation metrics in the academic world. Some see citation counts as a mark of scientific impact or even quality, but in reality the reasons for citing other work are manifold which makes the interpretation more complicated than a single citation count can reflect. Two years ago, the Journal of Cheminformatics proposed the CiTO Pilot for the adoption of a practice of annotating citations with their citation intentions. Basically, when you cite a journal article or dataset (or any other source), you also explain why specifically you cite that source. Particularly, the agreement and disagreement and reuse of methods and data are of interest. This article explores what happened after the launch of the pilot. We summarize how authors in the Journal of Cheminformatics used the pilot, shows citation annotations are distributed with Wikidata, visualized with Scholia, discusses adoption outside BMC, and finally present some thoughts on what needs to happen next.

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Section: Annotated Citation Networkmentioning

confidence: 99%

“…Research article DECIMER 1.0: deep learning for chemical image recognition using transformers [23] cito:agreesWith, cito:citesAsAuthority, cito:citesAsDataSource, cito:extends, cito:usesMethodIn 66% (*)…”

Section: Article Intentions %Citomentioning

confidence: 99%

Two years of explicit CiTO annotations

Willighagen

2023

J Cheminform

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“…A more complex alternative would be to extract the core and (in a generalised model) repeating unit structures, e.g., as SMILES strings. Current successes in similar applications are encouraging [ 80 ] but available training data would be a limiting factor, as the numbers of homologous structures detected in relevant datasets reported above and of published homologous series e.g., in specialised databases, appear too low for most machine learning tasks. However, defining core structures with chain attachment points and multiple repeating units structures may allow training data to be synthetically generated through recombination and enumeration to form diverse homologous series structures.…”

Section: Future Workmentioning

confidence: 99%

An algorithm to classify homologous series within compound datasets

et al. 2022

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Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH2 repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues.

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“…Another DL method proposed in 2017 that has recently gained popularity among the scientific community is transformers [ 34 ], which adopts the mechanism of self-attention to handle sequential data. They have been tested in a series of medical tasks, including cardiac abnormality diagnosis [ 35 ], food allergen identification [ 36 ], medical language understanding [ 37 ], and chemical image recognition [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

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DECIMER 1.0: deep learning for chemical image recognition using transformers

Cited by 47 publications

References 31 publications

Two years of explicit CiTO annotations

Two years of explicit CiTO annotations

An algorithm to classify homologous series within compound datasets

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

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