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

Introduction This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1. Authors: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun Dataset Description Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation. Citation We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field. Please cite as follows: Paper: Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” Computers in Biology and Medicine, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8. Dataset: Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.