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Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery

This is the anonymized training dataset for the article "Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery: The Use of Patient-Reported Outcome Measures", aimed at developing Machine Learning models for deciding on optimal pathway assignment for total joint replacement patients.

The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model

## Identification of transcription factor binding sites clustered regions First, the TFBSs were identified from ATAC-seq peaks by FIMO. The position-specific weight matrices (PWMs) of transcription factors were downloaded from CIS-BP databases. The genomic sequences under the open chromatin regions were used as inputs for FIMO with a custom library of all motifs for each species to scan for motif instances at a p-value threshold of 1e-5.  Then, an established method was used to identify TFCRs by performing the Gaussian kernel density estimations across the genome (with a bandwidth of 300bp centered on each TFBS). Each peak in density profile was considered a TFCR. To determine the complexity of each TFCR, the Gaussian kernelized distances from each peak that contributed at least 0.1 to its strength were determined. The complexity of each TFCR was determined by the quantity and proximity of the contributing TFBS. We combined motif instances based on the TF family information from CIS-BP to calculate the complexity of TFCR. The window for each TFCR was determined by finding the maximum distance (in bp) from the TFCR to a contributing TF and then adding 150 bp (one-half of the bandwidth). Each window was centered on the TFCR. The identified TFCR was grouped into 10 groups based on their complexity from low to high.  usage:    indir="Human_fimo" # the directory where you put the output files of FIMO    motifMap="Homo_sapiens_2020_0920/TF_Information_all_motifs_plus.txt" # the mapping relationship of TF and its TF family from CIS-BP    cd Codes/TFCR_embryo    perl d-motif_combine.pl $indir TFfamily $motifMap    perl e-tfpos_combine.pl TFfamily    perl f1-tf_bed-new-c.pl TFfamily    perl 0-merge-TFCR.pl $indir TFfamily

Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning

It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.

Data for "Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities"

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Instruction about codes and data produced by the study entitled: Diurnal carbon monoxide retrieval from FY-4B/GIIRS using a novel machine learning method

Instruction about codes and data produced by the study entitled: Diurnal carbon monoxide retrieval from FY-4B/GIIRS using a novel machine learning method. Please note that the manuscript is under review in a peer-reviewed journal.

Data and code for paper: Design Optimization of Geometric-Confined Cardiac Organoids Enabled by Machine Learning Techniques

Collection of the code for the generation of figures, analysis and data for the paper: Design Optimization of Geometric-Confined Cardiac Organoids Enabled by Machine Learning Techniques. Ensemble learning classifiers, PACMAP & Trimap code in python file. All figures and additional analysis in R code.

Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives

In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of 22 molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: high activity and low activity. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful candidates for effectively distinguishing between the two groups. Notably, the most informative descriptor for this separation relied solely on electronic populations and orbital energies. By understanding which computationally calculated properties are most relevant to specific biological activities, we can significantly enhance the efficiency of drug development processes, saving both time and resources.

Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.

opencc-by-4.0Apr 2024View details →

Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-ray Absorption Spectroscopy Data by Machine Learning

The repo contains all codes and data related to the JPCC publication entitled "Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-ray Absorption Spectroscopy Data by Machine Learning"

Machine Learning Tools for Peptide Bioactivity Evaluation Implications for Cell Culture Media Optimization and the Broader Cultivated Meat Industry

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Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)

This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)

This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Ripple: A Long-Sighted Self-Adaptation Approach to Retrain Machine-Learning-Enabled Systems

Data files required to reproduce the results of paper "Ripple: A Long-Sighted Self-Adaptation Approach to Retrain Machine-Learning-Enabled Systems" submitted to ICSME 2025

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)

This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)

This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)

This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning

Dataset containing different physical properties and gas loading capacity inside carbon molecular sieving membrane (CMSM) and metal organic frameworks (MOF). The physical properties of the adsorbent frameworks are used to train neural network (NN) and XGBoost machine learning models to accurately predict the gas loadings. This dataset corresponds to the publication, "Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning", Subhadeep Dasgupta, Amal RS, Prabal K Maiti.

Data used in the paper "Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser".

Data recorded for the experiment at the European XFEL are available at doi:10.22003/XFEL.EU-DATA-900331-00 and doi:10.22003/XFEL.EU-DATA-900383-00.

Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis

FragmentFactory_dataset is a pickled pandas DataFrame and should be loaded using the following code: <pre><code>import pandas as pd FF_data = pd.read_pickle('/my_directory/FragmentFactory_dataset.pkl')</code></pre>

Training data for building a machine learning wildfire model over the CONUS

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