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32 results for “similarity prediction”
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
KiSSim: Predicting off-targets from structural similarities in the kinome
<p><strong>KiSSim: Predicting off-targets from structural similarities in the kinome</strong></p> <p><strong>Project description.</strong></p> <p>KiSSim (Kinase Structural Similarity) is a novel fingerprint designed specifically for kinase pockets, allowing for similarity studies across the structurally covered kinome. The kinase fingerprint is based on the <a href="https://klifs.net/">KLIFS</a> pocket alignment, which defines 85 pocket residues for all kinase structures. This enables a residue-by-residue comparison without a computationally expensive alignment step.</p> <p>The pocket fingerprint encodes each pocket residue’s spatial and physicochemical properties. The spatial properties describe the residue’s position in relation to the kinase pocket center and important kinase subpockets, i.e. the hinge region, the DFG region, and the front pocket. The physicochemical properties encompass for each residue its size and pharmacophoric features, solvent exposure, and side chain orientation.</p> <p>Some datasets are not part of the `kissim_app` GitHub repository due to their size but can be downloaded from here to the respective kissim_app folders.</p> <p><strong>Data.</strong></p> <ul> <li>`20210902_KLIFS_HUMAN.tar.gz` --- save in `kissim_app/data/external/structures`</li> <li>`complete_SiteAlign.txt.gz` --- save in `kissim_app/data/external/sitealign`</li> </ul> <p><strong>Results.</strong></p> <ul> <li>`results.tar.bz2`--- save as `kissim_app/results`</li> </ul> <p>These are the KiSSim results: fingerprints, feature/fingerprint distances, kinase matrices, and kinase trees for structures in all (`all`), DFG-in (`dfg_in`), and DFG-out (`dfg_out`) conformation. In the case of the DFG-in conformation, we also have KiSSim runs with fingerprint subsets based on only residues that interact with certain ligands in KLIFS IFPs: Erlotinib (`dfg_in_IRE`), Imatinib (`dfg_in_STI`), Bosutinib (`dfg_in_DB8`), and Dopamapimod (`dfg_in_B96`). The folder contains README with a detailed file list.</p> <p><strong>Usage.</strong></p> <p>This dataset can be used to run the notebooks available on <a href="https://github.com/volkamerlab/kissim_app">https://github.com/volkamerlab/kissim_app</a>.</p> <ol> <li>Clone the kissim_app repository.</li> <li>Download the files provided here.</li> <li>If applicable, extract the archive content to the folders as indicated above and run the notebooks.</li> </ol> <pre><code class="language-bash">cd /path/to/your/download tar -xvf results.tar.bz2 -C /path/to/kissim_app/ tar -xvf 20210902_KLIFS_HUMAN.tar.bz2 -C /path/to/kissim_app/data/external/structures/ # In case you want the raw SiteAlign data mv complete_SiteAlign.txt.gz /path/to/kissim_app/data/external/sitealign</code></pre> <p><strong>Citation.</strong></p> <p>These datasets are part of the KiSSim publication: TBA</p>
Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "
<p>Datasets for preprint entitled "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi"</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of <em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi". </p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict. For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22. In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes. </p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset is made up of predicted protein tertiary structures representing the main member of each up-regulated <em>V. inaequalis</em> effector candidate family. Structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). In cases where the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2. Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold (<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>) open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input. </p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi" study. These structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input. </p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>
Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice
<p>Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal cooccurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. And we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.<span><br></span></p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
Different orthology inference algorithms generate similar predicted orthogroups among Brassicaceae species
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Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice
Open the record for dataset details and reuse information.
Taxonomic similarity does not predict necessary sample size for ex situ conservation: a comparison among five genera
<p>Effectively conserving biodiversity with limited resources requires scientifically informed and efficient strategies. Guidance is particularly needed on how many living plants are necessary to conserve a threshold level of genetic diversity in ex situ collections. We investigated this question for 11 taxa across five genera. In this first study analyzing and optimizing ex situ genetic diversity across multiple genera, we found that the percentage of extant genetic diversity currently conserved varies among taxa, from 40 to 95%. Most taxa are well below genetic conservation targets. Resampling datasets showed that ideal collection sizes vary widely even within a genus: one taxon typically required at least 50% more individuals than another (though Quercus was an exception). Still, across taxa, the minimum collection size to achieve genetic conservation goals is within one order of magnitude. Current collections are also suboptimal: they could remain the same size yet capture twice the genetic diversity with improved sampling design. We term this deficiency the "genetic conservation gap." Lastly, we show that minimum collection sizes are influenced by collection priorities regarding the genetic diversity target. In summary, current collections are insufficient (not reaching targets) and suboptimal (not efficiently designed), and we show how improvements can be made.</p>
Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities
<p>These codes are used in the paper entitled"Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities" which is submitted to the Journal of Water Resources Research. The code for the differentiable parameter learning (DPL) model can be downloaded at https://doi.org/10.5281/zenodo.7091334. The code for LSTM to reproduce our analysis is available at <a href="https://github.com/neuralhydrology/neuralhydrology">https://github.com/neuralhydrology/neuralhydrology</a>. The SWORD database utilized in our study can be accessed at https://zenodo.org/records/10013982. The geometric dataset of the global river attribute information for every river reach, the values of all pressure indicators (DOF, DOR,SED, USE, RDD and URB) and the values for the CSI are available at https://doi.org/10.6084/m9.figshare.7688801.</p>
Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects
<p>This uploads contains the datasets, phylogenetic tree and associated R code used to generate the results reported in the article: "Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects" published in Proceedings of the National Academy of Sciences USA (2024).<br>A detailed explanation of datasetS1 can be found in the supplementary data of the article. </p>
Taxonomic similarity does not predict necessary sample size for ex situ conservation: a comparison among five genera
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Data from: Ecological interactions and coexistence are predicted by gene expression similarity in freshwater green algae
Phenotypic variation controls the species interactions which determine whether or not species coexist. Long-standing hypotheses in ecology and evolution posit that phenotypic differentiation enables coexistence by increasing the size of niche differentiation. This hypothesis has only been tested using macroscopic traits to date, but niche differentiation, particularly of microscopic organisms, also occurs at the molecular and metabolic level. We examined how phenotypic variation that arises at the level of gene expression over evolutionary time affects phytoplankton species interactions and coexistence. We predicted that similarity in gene expression among species would decline with phylogenetic distance, and that reduced similarity in gene expression would weaken competition, increase facilitation and promote coexistence. To test this, we grew eight species of freshwater green algae in monocultures and bicultures for 46 days in a laboratory microcosm experiment. We quantified the strength of species interactions by: (i) fitting Lotka–Volterra models to time-series densities and estimating interaction coefficients, and (ii) calculating relative densities that compare species' steady-state densities in biculture to those in monoculture. We used Illumina high throughput sequencing to quantify the expression of 1253 families of homologous genes, including a set of 17 candidate genes that we hypothesized a priori to be involved in competition or facilitation. Synthesis. We found that closely related species had greater similarity in gene expression than did distantly related species, but as gene expression became more similar, species experienced weaker competition or greater facilitation, and were more likely to coexist. We identified gene functional categories that were uniquely differentially regulated in association with particular species interaction types. Contrary to common thinking in ecology and evolution, similarity in gene expression, and not differentiation, was associated with weaker competition, facilitation and coexistence.
Affiliation history and age similarity predict alliance formation in adult male bottlenose dolphins
Male alliances are an intriguing phenomenon in the context of reproduction since, in most taxa, males compete over an indivisible resource, female fertilization. Adult male bottlenose dolphins (<i>Tursiops aduncus</i>) in Shark Bay, Western Australia, form long-term, multi-level alliances to sequester estrus females. These alliances are therefore critical to male reproductive success. Yet, the long-term processes leading to the formation of such complex social bonds are still poorly understood. To identify the criteria by which male dolphins form social bonds with other males, we adopted a long-term approach by investigating the ontogeny of alliance formation. We followed the individual careers of 59 males for 14 years while they transitioned from adolescence (8-14 years of age) to adulthood (15-21 years old). Analyzing their genetic relationships and social associations in both age groups, we found that the vast majority of social bonds present in adolescence persisted through time. Male associations in early life predict alliance partners as adults. Kinship patterns explained associations during adolescence but not during adulthood. Instead, adult males associated with males of similar age. Our findings suggest that social bonds among peers, rather than kinship, play a central role in the development of adult male polyadic cooperation in dolphins.
Screening of 67 compounds with predicted biosignature profile similar to remdesivir for anti-SARS-CoV-2 activity
<p>This short report describes the most relevant results of screening compounds with a similar predicted biosignature as remdesivir in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields
<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication "Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields"</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
Gene structure prediction results and execution commands and options by GINGER and similar tools
<p>Gene structure prediction results, execution commands, and options by GINGER and similar tools</p> <ul> <li>genome.tar.gz ... genome sequence data used for benchmark test in the paper. </li> <li>input_of_EVM.tar.gz ... input dataset for EVM</li> <li>input_of_GINGER.tar.gz ... input dataset for GINGER</li> <li>input_of_MAKER.tar.gz ... input dataset for MAKER</li> <li>reference_annotation.tar.gz ... gff data used for benchmark test in the paper.</li> <li>result_of_EVM.tar.gz .... results and stats information of EVM (including intermediate result file of <em>C.elegans</em>, <em>D. melanogaster</em>, <em>O. sativa</em>, <em>D. rerio</em>, and <em>H. sapiens</em>)</li> <li>result_of_GINGER.tar.gz .... results and stats information of GINGER (including intermediate result files of <em>C.elegans</em>)</li> <li>result_of_MAKER.tar.gz .... results and stats information of MAKER (including intermediate result files of <em>C.elegans</em>)</li> </ul>
Data from: Ecological interactions and coexistence are predicted by gene expression similarity in freshwater green algae
Open the record for dataset details and reuse information.
Affiliation history and age similarity predict alliance formation in adult male bottlenose dolphins
Open the record for dataset details and reuse information.
Does evolutionary relatedness predict ecological similarity?
<p><span><span>A fundamental question in paleobiology is whether ecology is correlated with evolutionary history. By combining time-calibrated phylogenetic trees with genus occurrence data through time, we can understand how environmental preferences are distributed on a tree and evaluate support for models of ecological similarity. Exploring parameters that lend support to each evolutionary model will help address questions that lie at the nexus of the evolutionary and ecological sciences. </span></span><span>We calculated ecological difference and phylogenetic distance between species pairs for 83 taxa used in recent phylogenetic revisions of the brachiopod order Strophomenida. Ecological difference was calculated as the pairwise distance along gradients of water depth, carbonate, and latitudinal affinity. Phylogenetic distance was calculated as the pairwise branch length between tips of the tree. Our results show no relationship between ecological affinity and phylogeny. Instead results suggest an ecological burst during the initial radiation of the clade. This pattern likely reflects scaling at the largest macroevolutionary and macroecological scales preserved in the fossil record. Hierarchical scaling of ecological and evolutionary processes is complex, but phylogenetic paleoecology is an avenue for better evaluating these questions. </span></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.