Computers in Biology and Medicine

These are the QSAR data in the SDF format.SDF is one of a family of chemical-data file formats developed by MDL; it is intended especially for structural information. "SDF" stands for structure-data file, and SDF files actually wrap the molfile (MDL Molfile) format. We use the chemminer package to manage this format. QSAR_187.sdf contains the 187 compounds we selected through the literature for training the models. 5000compounds.zip, obviously contains 5000compounds.sdf - GitHub limits you to 25MB files 2nd-5000compounds.zip, obviously contains 2nd-5000compounds.sdf- GitHub limits you to 25MB files The other files are R code, these performed the data preprocessing, model building and analysis.

with these files, you can simulate all of the data which is shown in the paper.

MATLAB source code to reproduce the results in [1] by generating simulated atrial electrograms and implementing the proposed deconvolution approach to estimate local electrograms and LATs How to use it? in two steps: First simulate data by running "run_data_simulation.m". you can skip this part and use the simulated data in "data" folder to reproduce the images and results for tissue T4 in figure 2 of [1] Second run "run_transCurr_and_LAT_estimation.m" to implement deconvolution and LAT estimation, [1] B. Abdi, R. C. Hendriks, A.-J. van der Veen, and N. M. de Groot, "Improved Local Activation Time Annotation in Fractionated Atrial Electrograms Using Deconvolution", under review

Codes and datasets are being uploaded for other researchers to try them out

Dataset file including both preoperative and matched postoperative faces that have undergone orbital decompression surgery. (Updated 2020-01-03)

Attached is a zip file containing codes for simulated data generation, Lasso implementation and accuracy measurement

ACL Experimental Data

The images are based on Liquid-Based Cytology test from the pathology departments of a Chinese hospital and a biomedical device company. Under the guidance of a professional pathologist, we choose qualified images in numerous LCT cervical images. The dataset contains 104 images with size and each image has a ground truth that has been manually segmented by a professional pathologist.

======================================= BetaDL: a Protein Beta-sheet Predictor Utilizing a Deep Learning Model and Independent Set Solution Prediction of beta-sheet structures (beta-residue contacts, beta-strand pairs, beta-strand pairing directions) ======================================= Knowledge Engineering Research Group (KERG), Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Iran. Home page: https://kerg.um.ac.ir/ Contact: Dehgani.toktam@mail.um.ac.ir; naghibzadeh@um.ac.ir ======================================= Input: In order to run BetaDL the following information must be provided: 1. Protein sequence and secondary structure assignment 2. A residue contact Probabilities matrix 3. Probability model for beta-sheet size respect to the number of beta-strands in proteins Output: BetaDL will predict the following information about beta sheet structure: 1. Beta-residue contact maps 2. Beta-strand pairs 3. Beta-strand pairing directions ======================================= Installation (Windows Version) and usage: 1. Unzip "BetaDL_package.rar" into the "BetaDL_package" folder 2. Compile and build the source codes in "BetaDL" folder and move "BetaDL.exe" to the BetaDL_package 3. Provide input files: Residue contact Probabilities in the "residue contact probabilities" folder, e.g. "\residue contact probabilities\1e5ka.GCNN" Sequence and secondary structure in the "secondary structures" folder, e.g. "\secondary structures\1e5ka.out" 4. Run "BetaDL.exe" 5. Output file: Beta-residue contact probabilities, e.g. "\test\1e5ka_DeepBBContact.out" Beta-residue pairwise alignments, e.g. "\test\1e5ka_alignmnet.out" Beta-residue contact maps, "e.g. \test\1e5ka_contactmap.out" Beta-sheet structure, e.g. "\test\1e5ka_conformation.out" ****The correct answer for the sample protein (1e5ka) can be found in following address: "\correct_beta_sheet_structure\1e5ka_correct.out" Enjoy it! ======================================= For more information please contact: Dehgani.toktam@mail.um.ac.ir; naghibzadeh@um.ac.ir

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open-source codes of the proposed parrot optimizer (PO) is available at https://aliasgharheidari.com/PO.html.