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Dataset for Utilizing Pathway Dependency and Network Representation of Anticancer Drug Sensitivity for Drug Synergy Prediction

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opencc-zeroNov 2023View details →

dryad32/100

Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode

The dataset is the wave-induced geopotential output from linear ocean models and potential energy flux by a group-velocity-based wave energy flux scheme in the period (1992–2016), which is involved in building a lightweight model, as well as showing a simple instance of utilizing the wave energy transfer for the prediction of Atlantic Niño/Niñas.

opencc-by-4.0Apr 2023View details →

Dataset for "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model"

Data and plot scripts to create figures included in the paper draft "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model" (submitted to JAMES). This work has been funded by the German Science Foundation (DFG) under grant SE 1784/3-1, project ID 408011764 as part of the DFG priority program SPP 2115 on radar polarimetry.

The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".

This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.

King Rail (Rallus elegans) presence in the Midwestern United States is predicted by local-scale factors and avian community

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FIGURE 3 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)

FIGURE 3. (A) Specimen of the bromeliad Alcantarea imperialis with inflorescence photographed about 1 km from the type locality of Scinax v-signatus in the Municipality of Teresópolis, State of Rio de Janeiro. (B) A granitic outcrop with several individuals of A. imperialis, photographed in the Municipality of Miguel Pereira, State of Rio de Janeiro. (C) The same outcrop as it appears on Google Earth images. Yellow arrows indicate small whitish dots that represent individual bromeliads.

FIGURE 4 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)

FIGURE 4. Relief map of the State of Rio de Janeiro (Modified from http://www.agritempo.gov.br/altimetria/RJ.html) with the potential distribution of Alcantarea imperialis produced based on images of Google Earth (yellow circles), occurrence of Scinax v-signatus based on voucher specimens (blue stars), localities for Scinax insperatus (gray triangle), localities we identified A. imperialis on the field but were unable to detect using Google Earth (white square), and localities Based on A. imperialis from the Herbarium on the Jardim Botânico do Rio de Janeiro (Square) – http://www.jbrj.gov.br/.

FIGURE 2 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)

FIGURE 2. Map from GAA depicting the distribution of Scinax v-signatus. Note that the type locality (Black circle) was not included in distribution area.

FIGURE 1 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)

FIGURE 1. Map of southeastern Brazil highlighting the area of the States of Minas Gerais, Rio de Janeiro, and Espírito Santo. Symbols indicate the type localities for Scinax v-signatus (white circle), the type locality for S. arduous (white square) and S. belloni (black square). The white hexagons indicate three new localities for S. arduous. The star indicates two localities where an undescribed species was collected in the south of the State of the Espírito Santo, and the black circle indicates the locality for another undescribed species collected in the north of the State of Rio de Janeiro.

openMay 2023View details →

TRACE Dataset: Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

Features dataset, as described in Simonovsky, Eyal, et al. "Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation." Molecular Systems Biology (2023): e11407.Article: https://doi.org/10.15252/msb.202211407Code: https://github.com/eyalsim/trace

DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes

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GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction

The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.The dataset files are saved as .pkl file for the convenience of use.Paper Abstract:Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Sequence/simulation data for Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence

This is a DOI-linked deposition of sequence/biophysical properties pairs used in the associated paper by Lotthammer et al:Lotthammer, J. M.*, Ginell, G. M.*, Griffith, D.*, Emenecker, R. J. & Holehouse, A. S.  Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence. Nature Methods (in press), (2023).

PfamSDB, The structural database of Pfam seeds made by cutting the Full-Length Alphafold predicted structures

PfamSDB.tar contains the gzipped pdb file of Pfam seeds.PfamSDB_cutFS.tar.gz contains the Foldseek database of Pfam seeds made by cutting the database of Full-length proteins.

A data set for predicting pre-term birth.

A data set for predicting pre-term birth.

Accretion in the recurrent nova T CrB: Linking the superactive state to the predicted outburst

UBV photometry of the recurrent nova T CrB

Predicting the Habitat Suitability of Ilex verticillata in China with Field-Test Validations

The cut branches of Ilex verticillata are highly ornamental and have high economic value. Since its introduction to China, it has received widespread attention. In the context of climate change today, ensuring its promotion and sustainable production in China is of great significance. In this study, the MaxEnt (maximum entropy) model was used, combined with climate and soil variables, to assess the impact of climate change on its potential suitable habitat. We used 5430 I. verticillata occurrence data and validated the model prediction using extensive field testing (12 test sites located in areas from 23.19° N to 42.91° N and 76.17° E to 125.14° E). The habitat suitability model (AUC = 0.854) performed excellently. Among them, three precipitation variables and one temperature variable were the main factors determining the distribution of I. verticillata in China. Field trial tests and model predictions of the suitability of I. verticillata were consistent, indicating that our model predictions are biologically meaningful and economically valuable. Under the representative concentration pathway shared socioeconomic pathways (SSP) climate change scenarios, the high and medium suitable habitats for this species will be reduced in the future climate. This study helps to better understand the impact of climate change on I. verticillata and provides suggestions for the introduction and cultivation areas and protection of this species in China.

Ultra-sensitive ctDNA monitoring required for predicting response and resistance to immunotherapy in advanced melanoma

Data and supporting code for the manuscript "Ultra-sensitive ctDNA monitoring required for predicting response and resistance to immunotherapy in advanced melanoma".

Supplemental Data for the Journal Article Entitled Accelerating FEM-based Corrosion Predictions using Machine Learning submitted for publication to the Journal of the Electrochemical Society.

This repository contains the Supplemental Data for the Journal Article Entitled Accelerating FEM-based Corrosion Predictions using Machine Learning submitted for publication to the Journal of the Electrochemical Society. Authors: David Montes de Oca Zapiain 1, Demitri Maestas 1, Matthew Roop 1,2, Philip Noel 1, Michael Melia 1, Ryan Katona 1 1 Sandia National Laboratories, Albuquerque, NM 87185, USA  2 University of New Mexico, Albuquerque, NM 87131, USA Each folder contains a ReadMe.txt describing the files and their organization within each folder.  Acknowledgements: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. SAND No: SAND2023-14419O

Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables.