Guest Editors
Prof. Dr. Xiangtao Li, School of Artificial Intelligence, Jilin University, China.
lixt314@jlu.edu.cn
Dr. Yunhe Wang, School of Artificial Intelligence, Hebei University of Technology, China.
wangyh082@hebut.edu.cn
Summary
With the burst of the large-scale data in bioinformatics and computational biology disciplines, we have witnessed the explosive growth of different studies in cancer field. For instance, drug response prediction in cancer, drug sensitivity prediction of cancer cell lines, cancer prognosis prediction, and cancer-cell clustering. However, traditional studies always suffer from multitudes of challenges, including the dimensionality curse, data noises, data scalability, and data processing. To address these issues, novel computational methods and studies about cancer cells have to be developed. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when dealing with large amounts of data. It has become the hot topic in the field of artificial intelligence. Therefore, we can apply deep learning for the massive amounts of data.
Now we are reaching a new level of interest in the field with the emergence of many new applications and algorithms for deep learning. This Special Issue explores the latest deep learning algorithms and research results in the applications of cancer studies. We also welcome the application of novel algorithms and studies of deep learning in various fields about cancer, such as cancer diagnostics, cancer classification, and others.
We welcome authors to submit original research, review, and perspective articles focusing on, but not limited to, new findings in the following areas:
• Prediction of drug responses or sensitivity in cancer cell lines by deep learning
• Deep learning in cancer prognosis prediction
• Deep learning in high-dimensional data clustering and classification
• Cancer-cell deep learning clustering and classification
• Cancer detection and relevant gene identification using deep learning models
• Basic biological research on cancer by deep learning
Keywords
Deep Learning, Machine Learning, Cancer Data, High-Dimensional Data, Cancer Cells Research
Published Papers
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Open Access
ARTICLE
A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma
ZEKUN XIN, YUDAN MA, WEIQIANG SONG, HAO GAO, LIJUN DONG, BAO ZHANG, ZHILONG REN
BIOCELL, Vol.47, No.3, pp. 555-567, 2023, DOI:10.32604/biocell.2023.026254
（This article belongs to this Special Issue:
Application of Deep Learning in Cancer)
Abstract Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which
plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype
classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high
dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as
premature convergence.
Methods: To address those problems, we propose a new ant colony optimization (ACO)
algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different
diseases. In DACO,…
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Open Access
ARTICLE
SW-Net: A novel few-shot learning approach for disease subtype prediction
YUHAN JI, YONG LIANG, ZIYI YANG, NING AI
BIOCELL, Vol.47, No.3, pp. 569-579, 2023, DOI:10.32604/biocell.2023.025865
（This article belongs to this Special Issue:
Application of Deep Learning in Cancer)
Abstract Few-shot learning is becoming more and more popular in many fields, especially in the computer vision field. This inspires us to introduce few-shot learning to the genomic field, which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions. The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data. Accurate disease sub-type classification allows clinicians to efficiently deliver investigations and interventions in clinical practice. We propose the SW-Net, which simulates the clinical process of extracting the shared knowledge from a…
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