Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications.

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.

Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet.

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.

NanoForms: an integrated server for processing, analysis and assembly of raw sequencing data of microbial genomes, from Oxford Nanopore technology.

Background: Next Generation Sequencing (NGS) techniques dominate today's landscape of genetics and genomics research. Though Illumina still dominates worldwide sequencing, Oxford Nanopore is one of the leading technologies currently being used by biologists, medics and geneticists across various applications. Oxford Nanopore is automated and relatively simple for conducting experiments, but generates gigabytes of raw data, to be processed by often ambiguous set of alternative bioinformatics command-line tools, and genomics frameworks which require a knowledge of bioinformatics to run. Results: We established an inter-collegiate collaboration across experimentalists and bioinformaticians in order to provide a novel bioinformatics tool, free for academics. This tool allows people without extensive bioinformatics knowledge to simply process their raw genome sequencing data. Currently, due to ICT resources' maintenance reasons, our server is only capable of handling small genomes (up to 15 Mb). In this paper, we introduce our tool, NanoForms: an intuitive and integrated web server for the processing and analysis of raw prokaryotic genome data, coming from Oxford Nanopore. NanoForms is freely available for academics at the following locations: http://nanoforms.tech (webserver) and https://github.com/czmilanna/nanoforms (GitHub source repository).