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(1 - 14 of 14)

Kurchaba, S.; Vliet, J. van; Verbeek, F.J.; Veenman, C.J. 2023

Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning

Article / Letter to editor

open access

Starting from 2021, more demanding NOx emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to prioritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship NO2 estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI/S5P images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a methodology towards the automated and scalable selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data. It is based on a proposed regression model predicting the amount of NO2 that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced NO2 are integrated over observations of the ship in time and are used as a measure of the inspection worthiness of a ship. To add further evidence, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.

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Sbrollini, A.; Barocci, M.; Mancinelli, M.; Paris, M.; Raffaelli, S.; Marcantoni, I.; ... ; Burattini, L. 2023

Automatic diagnosis of newly emerged heart failure from serial electrocardiography by repeated structuring & learning procedure

Article / Letter to editor

open access

Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP... Show moreHeart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS & LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF data-base was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P >= 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS & LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable auto-matic HF diagnosis. Show less

Pratiwi Bunga, C.; Dusseldorp, E.; Karch, J.D.; de Rooij, M. 2023

Predictive performance of psychological tests: is it better to use items than subscales?

Article / Letter to editor

open access

Thomson, S.L.; Stein, N. van; Berg, D. van den; Leeuwen, C. van 2023

The opaque nature of intelligence and the pursuit of explainable AI

Article in monograph or in proceedings

open access

Balraadjsing, S.; Peijnenburg, W.J.G.M.; Vijver, M.G. 2022

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity

Article / Letter to editor

open access

Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the... Show moreEngineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets. Show less

Gorostiola Gonzalez, M.; Janssen, A.P.A.; IJzerman, A.P.; Heitman, L.H.; Westen, G.J.P. van 2022

Oncological drug discovery: AI meets structure-based computational research

Article / Letter to editor

open access

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these... Show moreThe integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster. Show less

Wall, H.E.C. van der; Hassing, G.J.; Doll, R.J.; Westen, G.J.P. van; Cohen, A.F.; Selder, J.L.; ... ; Gal, P. 2022

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: creation of a benchmark

Article / Letter to editor

open access

ObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging... Show moreObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated.Methods & resultsA total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12‑lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II.ConclusionThe application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments. Show less

Stel, W. van der; Yang, H.; Le Dévédec, S.E.; Water, B. van de; Beltman, J.B.; Danen, E.H.J. 2022

High-content high-throughput imaging reveals distinct connections between mitochondrial morphology and functionality for oxphos complex I, III, and V inhibitors

Article / Letter to editor

open access

Cells can adjust their mitochondrial morphology by altering the balance between mitochondrial fission and fusion to adapt to stressful conditions. The connection between a chemical perturbation,... Show moreCells can adjust their mitochondrial morphology by altering the balance between mitochondrial fission and fusion to adapt to stressful conditions. The connection between a chemical perturbation, changes in mitochondrial function, and altered mitochondrial morphology is not well understood. Here, we made use of high-throughput high-content confocal microscopy to assess the effects of distinct classes of oxidative phosphorylation (OXPHOS) complex inhibitors on mitochondrial parameters in a concentration and time resolved manner. Mitochondrial morphology phenotypes were clustered based on machine learning algorithms and mitochondrial integrity patterns were mapped. In parallel, changes in mitochondrial membrane potential (MMP), mitochondrial and cellular ATP levels, and viability were microscopically assessed. We found that inhibition of MMP, mitochondrial ATP production, and oxygen consumption rate (OCR) using sublethal concentrations of complex I and III inhibitors did not trigger mitochondrial fragmentation. Instead, complex V inhibitors that suppressed ATP and OCR but increased MMP provoked a more fragmented mitochondrial morphology. In agreement, complex V but not complex I or III inhibitors triggered proteolytic cleavage of the mitochondrial fusion protein, OPA1. The relation between increased MMP and fragmentation did not extend beyond OXPHOS complex inhibitors: increasing MMP by blocking the mPTP pore did not lead to OPA1 cleavage or mitochondrial fragmentation and the OXPHOS uncoupler FCCP was associated with OPA1 cleavage and MMP reduction. Altogether, our findings connect vital mitochondrial functions and phenotypes in a high-throughput high-content confocal microscopy approach that help understanding of chemical-induced toxicity caused by OXPHOS complex perturbing chemicals. Show less

Wardenaar, K.J.; Riese, H.; Giltay, E.J.; Eikelenboom, M.; Hemert, A.J. van; Beekman, A.F.; ... ; Schoevers, R.A. 2021

Common and specific determinants of 9-year depression and anxiety course-trajectories: a machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA)

Article / Letter to editor

open access

Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to... Show moreBackground: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (rho pred) were calculated. Results: Low to high prediction correlations (rho pred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course. Show less

Wall, H.E.C. van der; Doll, R.J.; Westen, G.J.P. van; Koopmans, I.; Zuiker, R.G.; Burggraaf, J.; Cohen, A.F. 2021

Using machine learning techniques to characterize sleep-deprived driving behavior

Article / Letter to editor

open access

OBJECTIVESleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the... Show moreOBJECTIVESleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions.METHODSData were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach.RESULTSThe sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness.CONCLUSIONWe developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs. Show less

Bruin, G.J. de; Veenman, C.J.; Herik, H.J. van den; Takes, F.W. 2021

Experimental evaluation of train and test split strategies in link prediction

Article in monograph or in proceedings

open access

In link prediction, the goal is to predict which links will appear in the future of an evolving network. To estimate the performance of these models in a supervised machine learning model, disjoint... Show moreIn link prediction, the goal is to predict which links will appear in the future of an evolving network. To estimate the performance of these models in a supervised machine learning model, disjoint and independent train and test sets are needed. However, objects in a real-world network are inherently related to each other. Therefore, it is far from trivial to separate candidate links into these disjoint sets.Here we characterize and empirically investigate the two dominant approaches from the literature for creating separate train and test sets in link prediction, referred to as random and temporal splits. Comparing the performance of these two approaches on several large temporal network datasets, we find evidence that random splits may result in too optimistic results, whereas a temporal split may give a more fair and realistic indication of performance. Results appear robust to the selection of temporal intervals. These findings will be of interest to researchers that employ link prediction or other machine learning tasks in networks. Show less

Barrientos, A.; Holdship, J.R.; Solar, M.; Martín, S.; Rivilla, V.M.; Viti, S.; ... ; Humire, P. 2021

Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks

Article / Letter to editor

open access

Sears, A.M. 2020

Algorithmic speech and freedom of expression

Article / Letter to editor

open access

Algorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have... Show moreAlgorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have to grapple. Courts in the United States have found that search engine results are a form of speech that is protected by the Constitution, and cases in Europe concerning liability for autocomplete suggestions have led to varied results. Beyond these instances, insight into how courts handle algorithmic speech are few and far between.By focusing on three categories of algorithmic speech, defined as curated production, interactive/responsive production, and semiautonomous production, this Article analyzes these various forms of algorithmic speech within the international framework for freedom of expression. After a brief introduction of that framework and a look towards approaches to algorithmic speech in the United States, the Article then examines whether the creators or controllers of different forms of algorithms should be considered content providers or mere intermediaries, the determination of which ultimately has implications for liability, which is also explored. The Article then looks at possible interferences with algorithmic speech, and how such interferences may be examined under the three-part test—particular attention is paid to the balancing of rights and interests at play—in order to answer the question of the extent to which algorithmic speech is worthy of protection under international standards of freedom of expression. Finally, other relevant issues surrounding algorithmic speech are discussed that will have an impact going forward, many of which involve questions of policy and societal values that accompany granting algorithmic speech protection. Show less

Brandsen, A.; Lambers, K.; Verberne, S.; Wansleeben, M. 2019

User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain

Article / Letter to editor

open access

In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named... Show moreIn this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we identified many possibilities for improvement. These entities were indexed in ElasticSearch and a user interface was developed on top of the index. This proof of concept was used in user requirement solicitation and evaluation with a group of end users. Feedback from this group indicated that there is a dire need for such a system, and that the first results are promising. Show less

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