State-of-the-art in artificial neural network applications: A survey.
Ontology highlight
ABSTRACT:
SUBMITTER: Abiodun OI
PROVIDER: S-EPMC6260436 | biostudies-literature | 2018 Nov
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.
| S-EPMC3735370 | biostudies-literature
Project description:Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.
| S-EPMC8435206 | biostudies-literature
Project description:Aptamers are single-stranded oligonucleotides with high affinity and specificity to the target molecules or cells, thus they can serve as an important category of molecular targeting ligand. Since their discovery, aptamers have been rapidly translated into clinical practice. The strong target affinity/selectivity, cost-effectivity, chemical versatility and safety of aptamers are superior to traditional peptides- or proteins-based ligands which make them unique choices for molecular imaging. Therefore, aptamers are considered to be extremely useful to guide various imaging contrast agents to the target tissues or cells for optical, magnetic resonance, nuclear, computed tomography, ultrasound and multimodality imaging. This review aims to provide an overview of aptamers' advantages as targeting ligands and their application in targeted imaging. Further research in synthesis of new types of aptamers and their conjugation with new categories of contrast agents is required to develop clinically translatable aptamer-based imaging agents which will eventually result in improved patient care.
| S-EPMC4410081 | biostudies-literature
Project description:State-of-the-art applications of nanomedicine have the potential to revolutionize the diagnosis, prevention, and treatment of a range of conditions and diseases affecting women's health. In this review, we provide a synopsis of potential applications of nanomedicine in some of the most dominant fields of women's health: mental health, sexual health, reproductive medicine, oncology, menopause-related conditions and dementia. We explore published studies arising from in vitro and in vivo experiments, and clinical trials where available, to reveal novel and highly promising therapeutic applications of nanomedicine in these fields. For the first time, we summarize the growing body of evidence relating to the use of nanomaterials as experimental tools for the detection, prevention, and treatment of significant diseases and conditions across the life course of a cisgender woman, from puberty to menopause; revealing the far-reaching and desirable theoretical impact of nanomedicine across different medical disciplines. We also present an overview of potential concerns regarding the therapeutic applications of nanomedicine and the factors currently restricting the growth of applied nanomedicine.
| S-EPMC5880180 | biostudies-other
Project description:The technological and clinical need for orthopedic replacement materials has led to significant advances in the field of nanomedicine, which embraces the breadth of nanotechnology from pharmacological agents and surface modification through to regulation and toxicology. A variety of nanostructures with unique chemical, physical, and biological properties have been engineered to improve the functionality and reliability of implantable medical devices. However, mimicking living bone tissue is still a challenge. The scope of this review is to highlight the most recent accomplishments and trends in designing nanomaterials and their applications in orthopedics with an outline on future directions and challenges.
| S-EPMC4592034 | biostudies-other
Project description:BackgroundIn Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys.ObjectiveThe aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model.MethodsData were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared.ResultsThe model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance.ConclusionsThis study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.
| S-EPMC10687680 | biostudies-literature
Project description:BackgroundThe Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown.MethodsIn this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration.ResultsThe training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD.ConclusionsWe demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.
| S-EPMC9872364 | biostudies-literature
Project description:An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identification of the most efficacious targets remains an important and challenging problem. Building on separate work that revealed a strong correlation between the inclusion of short sequence motifs and the activity level of an oligo, we have developed a predictive artificial neural network system for mapping tetranucleotide motif content to antisense oligo activity. Trained for high-specificity prediction, the system has been cross-validated against a database of 348 oligos from the literature and a larger proprietary database of 908 oligos. In cross- validation tests the system identified effective oligos (i.e. oligos capable of reducing target mRNA expression to <25% that of the control) with 53% accuracy, in contrast to the <10% success rates commonly reported for trial-and-error oligo selection, suggesting a possible 5-fold reduction in the in vivo screening required to find an active oligo. We have implemented a web interface to a trained neural network. Given an RNA transcript as input, the system identifies the most likely oligo targets and provides estimates of the probabilities that oligos targeted against these sites will be effective.
| S-EPMC140555 | biostudies-literature
Project description:In positron emission tomography (PET) image reconstruction, the Bayesian framework with various regularization terms has been implemented to constrain the radio tracer distribution. Varying the regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff between variance and spatial resolution measured from the reconstructed images. The purpose of this paper is to build a patch-based image enhancement scheme to reduce the size of the unachievable region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying mapping between the reconstructed image patches and an enhanced image patch. An artificial neural network model named multilayer perceptron (MLP) with backpropagation was used to solve this regression problem through learning from examples. Using the BrainWeb phantoms, we simulated brain PET data at different count levels of different subjects with and without lesions. The MLP was trained using the image patches reconstructed with a MAP algorithm of different regularization parameters for one normal subject at a certain count level. To evaluate the performance of the trained MLP, reconstructed images from other simulations and two patient brain PET imaging data sets were processed. In every testing cases, we demonstrate that the MLP enhancement technique improves the noise and bias tradeoff compared with the MAP reconstruction using different regularizing weights thus decreasing the size of the unachievable region defined by the MAP algorithm in the variance/resolution plane.
| S-EPMC6132251 | biostudies-literature
Project description:Concrete is the most used human-made material in the world, and it is responsible for around 8% of the total greenhouse gas emissions worldwide. Hence, efficient concrete construction methods are one of the main foci of research in architecture, civil engineering, and material science. One recent development that promises to achieve this goal is the use of digital fabrication for building components. Most investigations focus on direct extrusion 3D printing with concrete, which has already been covered in several review articles. Conversely, this article reviews a different approach, which focuses on the indirect digital fabrication of concrete through 3D printed formworks. This approach is under investigation for structural and nonstructural, as well as for on- and off-site applications, with a number of projects having already been built, but a comprehensive review of 3D printed formworks has not yet been compiled to synthesize the findings. This article provides a comprehensive map of the state-of-the-art of five different 3D printing technologies used for the fabrication of formworks so far. The aim is to serve as a fundamental reference for future research, provide a basis for consistent language in this field, and support the development of construction standards. The article further discusses the new geometric possibilities with 3D printed formworks and their potential for making concrete construction more sustainable. In addition, the opportunities and challenges of 3D printed formworks are evaluated in the context of other traditional and digital fabrication tools. A synthetic classification in five functional typologies is proposed and illustrated with 30 representative case studies. Finally, the article concludes with a brief reflection on the role of 3D printing in the broader context of formwork innovation and a possible outlook for this technology.
| S-EPMC9059241 | biostudies-literature