Special Issue Editor

Dr. Yunchen Yang

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Guest Editor

Scientist, Development of Data Science/Product, Sema4, Stamford, CT, USA
Interests: cancer diagnosis; biomarkers; precision oncology; artificial intelligence; medical/clinical informatics

Special Issue Information

Dear Colleagues,

Cancer is the leading cause of death worldwide. Given the molecular heterogeneity of tumorigenesis, molecular profiling technologies paired with statistical analytics are expected to provide comprehensive insight into each individual tumor and, in turn, improve the therapeutic landscape of cancer. The approach is also known as precision oncology. This concept emphasizes the demand of analytical tools with high resolution on molecular profiling and effective modeling strategy to interpret tumor omics data.

In recent years, tremendous breakthroughs in precision oncology have been made. Powered by novel techniques of imaging, microscopy, electrical, and photonics, numerous new analytical platforms have been developed, conducting accurate and efficient quantification of tumor molecular signatures. Various statistical and mathematical methodologies in the field of bioinformatics or medical informatics have also emerged to facilitate extensive data analysis.

In this Special Issue of Biomedinformatics, we invite contributions on advances in precision oncology in the field of biomedical information, with a focus on cancer diagnosis, prognosis, and therapeutic surveillance. Research topics include, but are not limited to, biomarker discovery, new methodologies facilitating the analysis of omics data, real world evidence analysis, translational informatics, and precision medicine of cancer. Both research articles and review articles are welcomed in this Special Issue.

Dr. Yunchen Yang
Guest Editor

Manuscript Submission Information

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Keywords

cancer diagnosis
cancer prognosis
cancer therapeutic
biomarkers
precision oncology
medical/clinical informatics
real world evidence
computational biology

Published Papers (1 paper)

Research

10 pages, 2895 KiB

Open AccessArticle

Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

by Siew Ming Kiu and Yin Chai Wang

BioMedInformatics 2022, 2(2), 234-243; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedinformatics2020015 - 29 Mar 2022

Cited by 1 | Viewed by 1804

Abstract

This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. Geometric features of blood cells are proposed to identify and classify overlapping cells into groups based on different overlapping degrees and the number of overlapped cells. In the proposed method, the percentage of average accuracy for identifying overlapping cells reached 98 percent. The proposed method can segment white blood cells from the overlapping cells with an overlapping degree of 70 percent. Improved Watershed Algorithm successfully increased 36.89 percent of accuracy in WBC segmentation. It reduced 46.12 percent of the over-segmentation problem. Tests of cell counting are conducted in the two stages, which are before and after the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31 percent, the average percentage of WBC counting is 84.8 percent, and the average percentage of RBC counting is 60.55 percent. The proposed method is efficient in identifying and classifying overlapping cells for increasing the accuracy of cell counting. Full article

(This article belongs to the Special Issue Advance of Precision Oncology in the Field of Biomedical Information — for Up-to-Date Practitioners)

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