Supervised
Dotaz
Zobrazit nápovědu
BACKGROUND: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. RESULTS: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. CONCLUSION: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.
- MeSH
- algoritmy MeSH
- biologické modely MeSH
- financování organizované MeSH
- mapování interakce mezi proteiny metody MeSH
- počítačová simulace MeSH
- proteom metabolismus MeSH
- regulace genové exprese fyziologie MeSH
- rozpoznávání automatizované metody MeSH
- signální transdukce fyziologie MeSH
- umělá inteligence MeSH
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (DOME) recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON, and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, and promoting transparency and reproducibility of ML in the life sciences.
- MeSH
- databáze faktografické MeSH
- lidé MeSH
- registrace * MeSH
- reprodukovatelnost výsledků MeSH
- řízené strojové učení * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods.
- MeSH
- algoritmy MeSH
- řízené strojové učení * MeSH
- strojové učení MeSH
- vysoká teplota * MeSH
- Publikační typ
- časopisecké články MeSH
In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration with motion control is an unexplored topic. In our work, we propose to utilize the LGMD neural network for visual interception detection with a central pattern generator (CPG) for locomotion control of a hexapod walking robot that are combined in the controller based on the long short-term memory (LSTM) recurrent neural network. Moreover, we propose self-supervised learning of the integrated controller to autonomously find a suitable setting of the system using a realistic robotic simulator. Thus, individual neural networks are trained in a simulation to enhance the performance of the controller that is then experimentally verified with a real hexapod walking robot in both collision and interception avoidance scenario and navigation in a cluttered environment.
- MeSH
- chování zvířat fyziologie MeSH
- chůze fyziologie MeSH
- kobylky fyziologie MeSH
- neuronové sítě MeSH
- řízené strojové učení MeSH
- robotika přístrojové vybavení MeSH
- učení vyhýbat se fyziologie MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.
The rather impressive extension library of medical image-processing platform 3D Slicer lacks a wide range of machine-learning toolboxes. The authors have developed such a toolbox that incorporates commonly used machine-learning libraries. The extension uses a simple graphical user interface that allows the user to preprocess data, train a classifier, and use that classifier in common medical image-classification tasks, such as tumor staging or various anatomical segmentations without a deeper knowledge of the inner workings of the classifiers. A series of experiments were carried out to showcase the capabilities of the extension and quantify the symmetry between the physical characteristics of pathological tissues and the parameters of a classifying model. These experiments also include an analysis of the impact of training vector size and feature selection on the sensitivity and specificity of all included classifiers. The results indicate that training vector size can be minimized for all classifiers. Using the data from the Brain Tumor Segmentation Challenge, Random Forest appears to have the widest range of parameters that produce sufficiently accurate segmentations, while optimal Support Vector Machines’ training parameters are concentrated in a narrow feature space.
- Publikační typ
- časopisecké články MeSH
Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.
- MeSH
- algoritmy * MeSH
- emoce MeSH
- lidé MeSH
- neuronové sítě * MeSH
- sémantika MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- MeSH
- dítě MeSH
- koně MeSH
- lidé MeSH
- mladiství MeSH
- pánev MeSH
- paréza rehabilitace MeSH
- rehabilitace metody MeSH
- skolióza rehabilitace MeSH
- terapie metody MeSH
- zakřivení páteře rehabilitace MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- srovnávací studie MeSH
Loss of olfactory function is a typical acute coronavirus disease 2019 (COVID-19) symptom, at least in early variants of SARS-CoV2. The time that has elapsed since the emergence of COVID-19 now allows for assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,...10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function. The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term. Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.
- MeSH
- anosmie diagnóza etiologie MeSH
- čich MeSH
- COVID-19 * MeSH
- lidé MeSH
- poruchy čichu * diagnóza MeSH
- řízené strojové učení MeSH
- RNA virová MeSH
- SARS-CoV-2 MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
