Exploring deep learning capabilities for surge predictions in coastal areas.
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ABSTRACT:
SUBMITTER: Tiggeloven T
PROVIDER: S-EPMC8390491 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.
| S-EPMC5219634 | biostudies-other
Project description:Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.
| S-EPMC5345877 | biostudies-literature
Project description:Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.
| S-EPMC8361087 | biostudies-literature
Project description:BackgroundDrug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue.MethodsWe propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles.ResultsOur proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources.ConclusionsWe find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.
| S-EPMC8379737 | biostudies-literature
Project description:Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.
| S-EPMC10427616 | biostudies-literature
Project description:Single-pixel imaging (SPI) has the advantages of high-speed acquisition over a broad wavelength range and system compactness. Deep learning (DL) is a powerful tool that can achieve higher image quality than conventional reconstruction approaches. Here, we propose a Bayesian convolutional neural network (BCNN) to approximate the uncertainty of the DL predictions in SPI. Each pixel in the predicted image represents a probability distribution rather than an image intensity value, indicating the uncertainty of the prediction. We show that the BCNN uncertainty predictions are correlated to the reconstruction errors. When the BCNN is trained and used in practical applications where the ground truths are unknown, the level of the predicted uncertainty can help to determine whether system, data, or network adjustments are needed. Overall, the proposed BCNN can provide a reliable tool to indicate the confidence levels of DL predictions as well as the quality of the model and dataset for many applications of SPI.
| S-EPMC10923550 | biostudies-literature
Project description:Deciphering the code of cis-regulatory element (CRE) is one of the core issues of today's biology. Enhancers are distal CREs and play significant roles in gene transcriptional regulation. Although identifications of enhancer locations across the whole genome [discriminative enhancer predictions (DEP)] is necessary, it is more important to predict in which specific cell or tissue types, they will be activated and functional [tissue-specific enhancer predictions (TSEP)]. Although existing deep learning models achieved great successes in DEP, they cannot be directly employed in TSEP because a specific cell or tissue type only has a limited number of available enhancer samples for training. Here, we first adopted a reported deep learning architecture and then developed a novel training strategy named "pretraining-retraining strategy" (PRS) for TSEP by decomposing the whole training process into two successive stages: a pretraining stage is designed to train with the whole enhancer data for performing DEP, and a retraining strategy is then designed to train with tissue-specific enhancer samples based on the trained pretraining model for making TSEP. As a result, PRS is found to be valid for DEP with an AUC of 0.922 and a GM (geometric mean) of 0.696, when testing on a larger-scale FANTOM5 enhancer dataset via a five-fold cross-validation. Interestingly, based on the trained pretraining model, a new finding is that only additional twenty epochs are needed to complete the retraining process on testing 23 specific tissues or cell lines. For TSEP tasks, PRS achieved a mean GM of 0.806 which is significantly higher than 0.528 of gkm-SVM, an existing mainstream method for CRE predictions. Notably, PRS is further proven superior to other two state-of-the-art methods: DEEP and BiRen. In summary, PRS has employed useful ideas from the domain of transfer learning and is a reliable method for TSEPs.
| S-EPMC6960260 | biostudies-literature
Project description:IntroductionOver the past 15 years, violent threats and acts against hospital patients, staff, and providers have increased and escalated. The leading area for violence is the emergency department (ED) given its 24/7 operations, role in patient care, admissions gateway, and center for influxes during acute surge events. This investigation had three objectives: to assess the current security of Washington State EDs; to estimate the prevalence of and response to threats and violence in Washington State EDs; and to appraise the Washington State ED security capability to respond to acute influxes of patients, bystanders, and media during acute surge events.MethodsA voluntary, blinded, 28-question Web-based survey developed by emergency physicians was electronically delivered to all 87 Washington State ED directors in January 2013. We evaluated responses by descriptive statistical analyses.ResultsAnalyses occurred after 90% (78/87) of ED directors responded. Annual censuses of the EDs ranged from < 20,000 to 100,000 patients and represented the entire spectrum of practice environments, including critical access hospitals and a regional quaternary referral medical center. Thirty-four of 75 (45%) reported the current level of security was inadequate, based on the general consensus of their ED staff. Nearly two-thirds (63%) of EDs had 24-hour security personnel coverage, while 28% reported no assigned security personnel. Security personnel training was provided by 45% of hospitals or healthcare systems. Sixty-nine of 78 (88%) respondents witnessed or heard about violent threats or acts occurring in their ED. Of these, 93% were directed towards nursing staff, 90% towards physicians, 74% towards security personnel, and 51% towards administrative personnel. Nearly half (48%) noted incidents directed towards another patient, and 50% towards a patient's family or friend. These events were variably reported to the hospital administration. After an acute surge event, 35% believed the initial additional security response would not be adequate, with 26% reporting no additional security would be available within 15 minutes.ConclusionOur study reveals the variability of ED security staffing and a heterogeneity of capabilities throughout Washington State. These deficiencies and vulnerabilities highlight the need for other EDs and regional emergency preparedness planners to conduct their own readiness assessments.
| S-EPMC5391897 | biostudies-literature
Project description:The application of deep learning techniques has led to substantial progress in solving a number of critical problems in machine vision, including fundamental problems of scene segmentation and depth estimation. Here, we report a novel deep neural network model, capable of simultaneous scene segmentation and depth estimation from a pair of binocular images. By manipulating the arrangement of binocular image pairs, presenting the model with standard left-right image pairs, identical image pairs or swapped left-right images, we show that performance levels depend on the presence of appropriate binocular image arrangements. Segmentation and depth estimation performance are both impaired when images are swapped. Segmentation performance levels are maintained, however, for identical image pairs, despite the absence of binocular disparity information. Critically, these performance levels exceed those found for an equivalent, monocularly trained, segmentation model. These results provide evidence that binocular image differences support both the direct recovery of depth and segmentation information, and the enhanced learning of monocular segmentation signals. This finding suggests that binocular vision may play an important role in visual development. Better understanding of this role may hold implications for the study and treatment of developmentally acquired perceptual impairments.
| S-EPMC8300045 | biostudies-literature
Project description:Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications in the setting of observational data, deep learning methods must be made compatible with the required causal assumptions. We present a scenario with real-world medical images (CT-scans of lung cancer) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity), respectively. When a deep network would use all the information available in the image to predict survival, it would condition on the collider and thereby introduce bias in the estimation of the treatment effect. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of linear independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long-standing goal of personalized medicine supported by artificial intelligence.
| S-EPMC6904461 | biostudies-literature