Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

The datasets used in the publications titled "Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications"

Modeling management strategies for chronic disease in wildlife: predictions for the control of respiratory disease in bighorn sheep

1. Controlling persistent infectious disease in wildlife populations is an on-going challenge for wildlife managers and conservationists worldwide. 2. Here, we develop a dynamic pathogen transmission model capturing key features of M. ovipneumoniae infection, a major cause of population declines in North American bighorn sheep (Ovis canadensis). We explore the effects of model assumptions and parameter values on disease dynamics, including density versus frequency dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease-induced mortality and recovery rates, and the epidemic growth rate. 3. We compare the effectiveness of a suite of management actions following an epidemic, including test-and-remove, depopulation-and-reintroduction, range expansion, herd augmentation, and density reduction. 4. Our results suggest that test-and-remove, depopulation-and-reintroduction, and range expansion have the potential to facilitate recovery of persistently infected bighorn sheep herds post-epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Management that improves host survival or reduces disease-induced mortality are also likely to improve population size and persistence of chronically infected herds. 5. Dynamic transmission models like the one employed here offer a structured, logical approach towards exploring hypotheses and can serve as a basis for planning field experiments and adaptive management. Models should be used iteratively with the field empirical approaches to triangulate on better approaches to wildlife management.

opencc-by-4.0Feb 2022View details →

Deficits of hierarchical predictive coding in left spatial neglect

Right brain-damaged patients with unilateral spatial neglect fail to explore the left side of space. Recent EEG and clinical evidence suggests that neglect patients might suffer deficits in predictive coding, i.e. in identifying and exploiting probabilistic associations among sensory stimuli in the environment. To gain direct insights on this issue, we focussed on the hierarchical components of predictive coding. We recorded EEG responses evoked by central, left-side or right-side tones that were presented at the end of sequences of four central tones. Left-side and right-side deviant tones produce a pre-attentive Mismatch Negativity that reflects a lower-order prediction error for the ‘Local’ deviation of the tone at the end of the sequence. Higher-order prediction errors for the frequency of these deviations in the acoustic environment, i.e. ‘Global’ deviation, are marked by the P3 response. We show that when neglect patients are immersed in an acoustic environment characterized by frequent left-side deviant tones, they display no pre-attentive Mismatch Negativity both for left-side deviant tones and infrequent omissions of the last tone, while they have Mismatch Negativity for infrequent right-side deviant tones. In the same condition, neglect patients show no P300 response to ‘Global’ prediction errors for deviant tones, including those in the non-neglected right-side, and omissions. In contrast to this, when right-side deviant tones are predominant in the acoustic environment, neglect patients have pre-attentive Mismatch Negativity both for right-side deviant tones and infrequent omissions, while they display no Mismatch Negativity for infrequent left-side deviant tones. Most importantly, in the same condition neglect patients show enhanced P300 response to infrequent left-side deviant tones, notwithstanding that these tones evoked no pre-attentive Mismatch Negativity. This latter finding indicates that ‘Global’ predictions are independent of ‘Local’ error signals provided by the Mismatch Negativity. These results qualify deficits of predictive coding in the spatial neglect syndrome and show that neglect patients base their predictive behaviour only on statistical regularities that are related to the frequent occurrence of sensory events on the right side of space.

Neuronal activity in sensory cortex predicts the specificity of learning

Learning to avoid dangerous signals while preserving normal responses to safe stimuli is essential for everyday behavior and survival. Following identical experiences, subjects exhibit fear specificity ranging from high (specializing fear to only the dangerous stimulus) to low (generalizing fear to safe stimuli), yet the neuronal basis of fear specificity remains unknown. Here, we identified the neuronal code that underlies inter-subject variability in fear specificity using longitudinal imaging of neuronal activity before and after differential fear conditioning in the auditory cortex of mice. Neuronal activity prior to, but not after learning predicted the level of specificity following fear conditioning across subjects. Stimulus representation in auditory cortex was reorganized following conditioning. However, the reorganized neuronal activity did not relate to the specificity of learning. These results present a novel neuronal code that determines individual patterns in learning. Keywords: fear conditioning, auditory cortex, sensory systems, learning, computational model, imaging, sensory cortex, tuning curve, neurobiology, population coding.

opencc-by-4.0Feb 2022View details →

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis

data files, including ML models of dynamic classifiers, trajectories of electronic structure and geometric features, optimized geometries, and final csv files.

Data from: Crop health is predicted by soil microbial diversity across phylogenetic scales

Soils contain diverse living communities that provide key ecosystem functions in agroecosystems. In many systems, ecosystems functions are positively related to the taxonomic, phylogenetic, and functional diversity of the community. Despite calls to incorporate microbial diversity in measures of soil health, whether increased microbial diversity per se can predict increased crop health and productivity has rarely been documented. Here we used microbial communities from commercial potato fields varying in diversity and composition, and experimentally assessed their ability to promote crop yield under low or high nutrient conditions and to suppress a soil-borne pathogen. Across two independent sets of communities, we found that yields under low nutrient conditions were predicted by high initial microbial diversity measured at broad phylogenetic levels, consistent with greater niche complementarity among unrelated taxa leading to greater total resource use. However, disease suppression was inconsistently linked to diversity and explained as well or better by microbial composition rather than diversity per se. Ecosystem multifunctionality was predicted by high diversity at broad to intermediate phylogenetic scales. These results indicate that the diversity of microbial taxa may influence multiple soil functions; however, the mechanisms underlying the diversity-function relationships may vary.

The 'dark matter' of protein variants carries a distinct DNA signature and predicts damaging variant effects

Data generated for the manuscript "The 'dark matter' of protein variants carries a distinct DNA signature and predicts damaging variant effects".   Please consult the README file for detailed description of data files included in this dataset.

Data from: How far is enough? Prediction of the scale of effect for wild bees

A crucial issue for landscape ecologists is identifying the spatial extents at which a landscape affects species occurrence. Multi-scale analyses are usually conducted to identify the "scale of effect", that is, the spatial extent associated with the best relationship between landscape variables and species occurrence, which is assumed to be related to species traits. However, few guidelines exist to determine the range of distances to be investigated. Based on the foraging distances of wild bee species, our main goal was to estimate the maximum distance of effect, that is, the distance beyond which the scale of effect for wild bee species is unlikely to be detected. Using the InVEST pollination model, we (i) modelled bee categories with distinct foraging distances and identified the scale of effect on their simulated abundance (ii) defined an index, noted λ, that estimates the distance beyond which landscape composition has only negligible effects on simulated abundances. We validated our results by identifying the scale of effect on the abundances of 16 bee species collected in south-western France. We detected a significant positive relationship between the average foraging distance (α) of the modelled bees and their scale of effect. The λ index was linearly related to the average foraging distances of bees (λ=5.4 α+253) and was above the identified scale of effect for the modelled bees. The λ was also found to be above the scale of effect for 93% of the observed bee species. Our results suggest that the λ index is a good estimator of the upper limit of the scale of effect for wild bees. The λ index could be used to identify the minimum distance between sampling sites before setting up an experiment and the maximum buffer size required in multi-scale analysis to detect the scale of effect.

Saluki: Predicting mRNA half-life from mRNA sequence

Here we release datasets for Saluki, a hybrid deep convolutional and recurrent neural network, whose goal is to predict mRNA half-life from an mRNA sequence. Dataset can be analyzed with our associated Github code (https://github.com/vagarwal87/saluki_paper).

Characteristics of COVID-19 patients predicting hospital readmission after ED discharge

Objective: We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalization/death, in COVID-19 patients previously discharged from the ED. We also developed a disease progression velocity index. Design and setting: Retrospective cohort study of prospectively collected data. The charts of consecutive COVID-19 patients discharged from the Reggio Emilia (Italy) ED (March 2 - March 31, 2020) were retrospectively examined. Clinical, laboratory, and computed tomography (CT) findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index. Participants: 450 patients discharged from the ED with diagnosis of COVID-19. Main outcome measure: ED readmission within 14 days, followed by hospitalization/death. Results: Of the discharged patients, 84 (18.7%) were readmitted to the ED and 61 (13.6%) were hospitalized and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03-1.08), Charlson Comorbidity Index 3 vs 0 (OR=11.61; 95% CI 1.76-76.58), days from symptom onset (OR for one day increase=0.81; 95% CI 0.73-0.90), and CT extension (OR for 1% increase=1.03; 95% CI 1.01-1.06) were associated in a multivariable model for readmission with hospitalization/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08-1.36); the model including this index resulted in less information loss. Conclusions: A velocity index combining CT extension and days from symptom onset predicts disease progression in COVID-19 patients. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

Predicting the failure of two-dimensional silica glasses

Datasets for the paper: Font-Clos F, Zanchi M, Hiemer S, Bonfanti S, Guerra R, Zaiser M, Zapperi S. Predicting the failure of two-dimensional silica glasses. arXiv preprint arXiv:2201.09723. 2022 Jan 24. <a href="https://arxiv.org/abs/2201.09723">https://arxiv.org/abs/2201.09723</a> The datasets can be used together with the codes available at <a href="https://github.com/ComplexityBiosystems/2D-silica-ML">https://github.com/ComplexityBiosystems/2D-silica-ML</a> to reproduce the resuts of the paper above. In particular, the datasets contain the the following data <ul> <li>The dataset '20201222-var02-onethousand.zip' contains configurations at fixed disorder (s2= 0.2).</li> <li>The dataset '20210209-variable-disorder.zip' contains configurations at variable disorder.</li> <li>The dataset 'ml_dataset_fracture_atoms_var02.tar.gz' contains the images for the prediction of the crack path.</li> <li>The dataset 'ml_dataset_var02.tar.gz' contains images of silica at fixed disorder (s2= 0.2). for the prediction of disorder, rupture strain and location.</li> <li>The dataset 'ml_dataset_variable_var.tar.gz' contains images of silica at variable disorder for the prediction of disorder, rupture strain and location.</li> <li>The dataset 'trained_models' contains an example of a ResNet trained for predicting rupture strain at variable disorder (it can be used for testing the Grad-CAM method).</li> </ul>

The living heart: climate gradients predict desert mountain endemism

Mountain regions are centres of biodiversity endemism at a global scale but the role of arid- zone mountain ranges in shaping biodiversity patterns is poorly understood. Focusing on three guilds of taxa from a desert upland refugium in Australia, we sought to determine: 1) the relative extent to which climate, terrain or geological substrate predict endemism, and 2) whether patterns of endemism are complimentary across broad taxonomic guilds. We mapped regional endemism for plants, land snails and vertebrates using combined Species Distribution Models (SDMs) for all endemic taxa (n=82). We then modelled predictors of endemism using Generalised Additive Models (GAMs) and geology, terrain and climate variables. We tested for the presence of inter- and intraguild hotspots of endemism. Many individual plant and land snail taxa were tightly linked with geology, corresponding to small distributions. Conversely, most vertebrate taxa were not constrained to specific geological substrates and occurred over larger areas. However, across all three guilds, climate was the strongest predictor of regional endemism, particularly for plants wherein discrete hotspots of endemism were buffered from extreme summer temperatures. Land snail and vertebrate endemism peaked in areas with the highest precipitation in the driest times of the year. Hotspots of endemism within each guild poorly predicted endemism in other guilds. We found an overarching signal that climatic gradients play a dominant role in the persistence of endemic taxa in an arid-zone mountain range system. An association with higher rainfall and cooler temperatures indicates that continuing trends towards hotter and drier climates may lead to range contractions in this, and potentially other, arid-zone mountain biotas. Contrasting patterns of endemism across guilds highlight the need to couple comprehensive regional planning for the protection of climate refugia, with targeted management of more localised and habitat specialist taxa.

Predictive modeling for clinical features associated with Neurofibromatosis Type 1

Objective: Perform a longitudinal analysis of clinical features associated with Neurofibromatosis Type 1 (NF1) based on demographic and clinical characteristics, and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort. Methods: Using NF1 as a model system, we perform retrospective data analyses utilizing a manually-curated NF1 clinical registry and electronic health record (EHR) information, and develop machine-learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis. Results: Males and females were evenly represented in the cohort. White children were more likely to develop OPG (OR: 2.11, 95%CI: 1.11-4.00, p=0.02) relative to their non-white peers. Median age at diagnosis of OPG was 6.5 years (1.7-17.0), irrespective of sex. Males were more likely than females to have a diagnosis of ADHD (OR: 1.90, 95%CI: 1.33-2.70, p<0.001), and earlier diagnosis in males relative to females was observed. The gradient boosting classification model predicted diagnosis of ADHD with an AUROC of 0.74, and predicted diagnosis of OPG with an AUROC of 0.82. Conclusions: Using readily available clinical and EHR data, we successfully recapitulated several important and clinically-relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naïve machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.

Interaction of diet and habitat predicts Toxoplasma gondii infection rates in wild birds at a global scale

Aim: Free-ranging wildlife are valuable sentinels for zoonotic, multi-host pathogens, and novel insight on parasite transmission patterns is possible through a macroecological approach. Toxoplasma gondii is a protozoan capable of infecting all warm-blooded animals, including humans, primarily through a free-living oocyst and/or tissue cyst life-stage. Anthropogenic disturbance is facilitating the spread of T. gondii, making it critical to understand the general ecological and life history drivers of T. gondii infections in wild birds, which are important intermediate hosts. Our goal was to determine how habitat (terrestrial vs. aquatic), dietary trophic level and scavenging behaviour influence T. gondii infection prevalence in wild birds on a global scale. Location: Global Time period: 1952-2017 Major taxa studied: Birds Methods: Our analysis used the serological, bioassay and molecular prevalence data of T. gondii in avian species compiled from 81 studies conducted worldwide and encompassing 24,344 individuals from 393 avian species from 84 families. Results: We show that at a global scale, trophic level and habitat significantly interact to influence T. gondii prevalence in avian intermediate hosts. In the terrestrial environment, T. gondii prevalence increases with trophic level, consistent with predominant tissue cyst transmission. The highest prevalence was in terrestrial omnivores, which may reflect their synanthropic foraging behaviour. In aquatic species, prevalence was more consistent across trophic levels, but high prevalence in aquatic herbivores and insectivores reflects significant waterborne exposure to oocysts. Contrary to our predictions, generalized scavenging per se was not associated with increased prevalence. Main conclusions: This study highlights the value of comparing pathogen prevalence among multiple ecological guilds and ecosystem types for a comprehensive understanding of the epidemiology of generalist pathogens, such as T. gondii. Increased effort is needed to reduce T. gondii spillover from the domestic cat cycle into wildlife populations.

opencc-zeroDec 2022View details →

Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs-based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between-version differences). To this aim, we simulated occurrence probability and presence-absence across Europe of four virtual species (VS) with differing climate-occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate-occurrence relationships for the climate-tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between-version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between-version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between-version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re-evaluating previous biodiversity-related works relying on geographical predictions from Worldclim-based HSMs.

Tolerance niche expansion and potential distribution prediction during Asian openbill bird range expansion

It is prevalent to use ecological niche models in the analysis of species expansion and niche changes. However, it is difficult to estimate the niche when alien species fail to establish in exotic areas. Here, we applied the tolerance niche concept, which means that niche of species can live and grow but preclude a species from establishing self-sustaining populations, in such fail-to-establish events. Taking the rapidly expanded bird, Asian openbill (Anastomus oscitans), as a model species, we investigated niche dynamics and its potential effects on the population by Niche A and ecospat, predicted potential distribution by biomod2. Results showed that niche expansion has occurred in two non-native populations caused by the tolerance of colder and wetter environments, and potential distribution mainly concentrated on equatorial islands. Our study suggested that the expanded niche belongs to tolerance niche concept according to the populations' dynamics and GPS tracking evidence. It is essential to consider source populations when we analyse the alien species. We recommended more consideration to the application of tolerance niche in alien species research, and there is still a need for standard measurement frameworks for analysing the tolerance niche.

opencc-by-4.0Nov 2021View details →

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

This dataset contains all (>1000) cell pairs as the Blue Brain Projects <a href="https://github.com/BlueBrain/EModelRunner">EModelRunner</a> packages as well as analysis code and analysed data used for the figures of our <a href="https://www.biorxiv.org/content/biorxiv/early/2020/04/20/2020.04.19.043117.full.pdf">preprint</a>: "A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex". More documentation will follow in the upcoming days. Updates: v1.1 (31/12/2021): updated READMEs within cell_packages, added 2 extra authors for their contribution in EModelRunner. v1.2 (31/12/2021): same as v1.1 but w/o MacOS junk v1.3 (11/01/2022): added analysis notebooks v2.0 (11/01/2022): same as v1.3 but w/o MacOS junk and proper version number v2.1 (14/03/2022): fetching data from websites when possible instead of providing the downloaded csv files.

CT-Based Radiomics Analysis to predict histo-pathological outcomes following liver resection in Colorectal Liver Metastases

I uploaded the images of the manuscript "CT-Based Radiomics Analysis to predict histo-pathological outcomes following liver resection in Colorectal Liver Metastases". The objective of the study was to assess the radiomics features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with liver colon rectal metastases in order to predict histo-pathological outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.

opencc-zeroDec 2011View details →

Data from: The prediction of adaptive evolution: empirical application of the secondary theorem of selection and comparison to the breeder's equation

Adaptive evolution occurs when fitness covaries with genetic merit for a trait (or traits). The breeder's equation (BE), in both its univariate and multivariate forms, allows us to predict this process by combining estimates of selection on phenotype with estimates of genetic (co)variation. However, predictions are only valid if all factors causal for trait-fitness covariance are measured. While this requirement will rarely (if ever) be met in practice, it can be avoided by applying Robertson's secondary theorem of selection (STS). The STS predicts evolution by directly estimating the genetic basis of trait-fitness covariation with out any explicit model of selection. Here we apply the BE and STS to four morphological traits measured in Soay sheep (Ovis aries) from St. Kilda. Despite apparently positive selection on heritable size traits, sheep are not getting larger. However, while the BE predicts increasing size the STS does not, a discrepancy that suggests unmeasured factors are upwardly biasing our estimates of selection on phenotype. We suggest this is likely to be a general issue, and that wider application of the STS could offer at least a partial resolution to the common discrepancy between naive expectations and observed trait dynamics in natural populations.

Virus isolation data improve host predictions for New World rodent orthohantaviruses

Data and code for "Virus isolation data improve host predictions for New World rodent orthohantaviruses" in Journal of Animal Ecology