| 1. |
- Bengtsson, Marie, et al.
(author)
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Structure-activity relationships for chain-shortened analogs of (Z)-5-decenyl acetate, a pheromone component of the turnip moth, Agrotis segetum
- 1990
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In: Journal of Chemical Ecology. - 0098-0331. ; 16:3, s. 667-684
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Journal article (peer-reviewed)abstract
- Structure-activity relationships for chain-shortened analogs of (Z)-5-decenyl acetate, a pheromone component of the turnip moth, Agrotis segetum, have been studied by electrophysiological single-sensillum technique and interpreted in terms of a previously reported receptor-interaction model. The results indicate that the terminal methyl group, as well as the acetate group, interacts with highly complementary receptor sites. The terminal alkyl chain is suggested to interact with a hydrophobic "pocket" extending over the two methylene groups closest to the terminal methyl group. The amounts of stimulus actually released from the odor source have been studied. The results demonstrate the necessity to take differences of volatility into account in comparisons of electrophysiological data for compounds of different chain lengths. It is shown that relative vapor pressures may to a good approximation be employed to estimate correction factors.
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| 4. |
- Chandra, Abel, et al.
(author)
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Transformer-based deep learning for predicting protein properties in the life sciences
- 2023
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In: eLIFE. - : eLife Sciences Publications. - 2050-084X. ; 12
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Journal article (peer-reviewed)abstract
- Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model - the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
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| 5. |
- de Pierrefeu, Amicie, et al.
(author)
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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine-learning with structured sparsity
- 2018
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In: Acta Psychiatrica Scandinavica. - : John Wiley & Sons. - 0001-690X .- 1600-0447. ; 138, s. 571-580
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Journal article (peer-reviewed)abstract
- ObjectiveStructural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross‐sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings’ reproducibility.MethodWe propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross‐site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first‐episode patients.ResultsMachine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first‐episode psychosis patients (73% accuracy).ConclusionThese results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.
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| 6. |
- De Pierrefeu, Amicie, et al.
(author)
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Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity
- 2018
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In: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). - : IEEE. - 9781538668597
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Conference paper (peer-reviewed)abstract
- The use of machine-learning (ML) in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Indeed, ML algorithms can jointly examine all brain features to capture complex relationships in the data in order to make inferences at a single-subject level. To deal with such high dimensional input and the associated risk of overfitting on the training data, a proper regularization (or feature selection) is required. Standard ℓ2-regularized predictors, such as Support Vector Machine, provide dense patterns of predictors. However, in the context of predictive disease signature discovery, it is now essential to understand the brain pattern that underpins the prediction. Despite ℓ1-regularized (sparse) has often been advocated as leading to more interpretable models, they generally lead to scattered and unstable patterns. We hypothesize that the integration of prior knowledge regarding the structure of the input images should improve the relevance and the stability of the predictive signature. Such structured sparsity can be obtained by combining together ℓ1 (possibly ℓ2) and Total variation (TV) penalties. We demonstrated the relevance of using ML with structured sparsity on a large multisite dataset of schizophrenia patients and controls. Using 3D maps of grey matter density, we obtained promising inter-site prediction performances. More importantly, we have uncovered a predictive signature of schizophrenia that is clinically interpretable and stable across resampling. This suggests that structured sparsity provides a major breakthrough over 'off-The-shelf' algorithms to perform a robust selection of important brain regions in the context of biomarkers discovery.
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| 7. |
- de Pierrefeu, Amicie, et al.
(author)
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Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity
- 2018
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In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 39:4, s. 1777-1788
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Journal article (peer-reviewed)abstract
- Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.
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| 8. |
- de Pierrefeu, Amicie, et al.
(author)
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Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty
- 2018
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In: IEEE Transactions on Medical Imaging. - : IEEE. - 0278-0062 .- 1558-254X. ; 37:2, s. 396-407
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Journal article (peer-reviewed)abstract
- Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA’s interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered andunstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., l1 and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV’s effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., N-dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.
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| 9. |
- Dubois, Mathieu, et al.
(author)
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Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI
- 2014
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Conference paper (peer-reviewed)abstract
- The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l12 penalty) or scattered (l1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of l1, l2, and TV penalties while preserving the exact l1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.
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| 10. |
- Fetty, Lukas, et al.
(author)
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Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion
- 2020
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In: Physics in Medicine and Biology. - : Institute of Physics Publishing (IOPP). - 0031-9155 .- 1361-6560. ; 65:10
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Journal article (peer-reviewed)abstract
- Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.
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