Special Issues
Table of Content

Application of Deep Learning in Cancer

Submission Deadline: 01 October 2023 (closed) View: 1

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


  • Open Access

    REVIEW

    Possible therapeutic role of short-chain fatty acids from skin commensal bacteria in UVB-induced skin carcinogenesis

    PAVITHRA SUBRAMANI, RAUNAK KUMAR DAS
    BIOCELL, Vol.47, No.10, pp. 2195-2205, 2023, DOI:10.32604/biocell.2023.030383
    (This article belongs to the Special Issue: Application of Deep Learning in Cancer)
    Abstract Solar ultraviolet B (UVB) radiation is a major skin cancer-causing agent. Initiation, promotion, and progression are the diverse phases of UVB-induced carcinogenesis. Exposure to UVB causes abnormalities in a series of biochemical and molecular pathways: thymine dimer formation, DNA damage, oxidative stress, inflammatory responses, and altered cell signaling, eventually resulting in tumor formation. The increased skin cancer rates urge researchers to develop more efficient drugs, but synthetic chemotherapeutic drugs have more contrary effects and drug resistance issues, which have been reported recently. The current review focuses on the relationship between microbes and cancer. Human skin… More >

    Graphic Abstract

    Possible therapeutic role of short-chain fatty acids from skin commensal bacteria in UVB-induced skin carcinogenesis

  • Open Access

    ARTICLE

    Heterogeneity beyond tumor heterogeneity—SULF2 involvement in Wnt/β-catenin signaling activation in a heterogeneous side population of liver cancer cells

    DONGYE YANG, DONGDONG GUO, YUNMEI PENG, DONGMENG LIU, YANQIU FU, FEN SUN, LISHI ZHOU, JIAQI GUO, LAIQING HUANG
    BIOCELL, Vol.47, No.9, pp. 2037-2049, 2023, DOI:10.32604/biocell.2023.028863
    (This article belongs to the Special Issue: Application of Deep Learning in Cancer)
    Abstract Introduction: Sulfatase 2 (SULF2), an endogenous extracellular sulfatase, can remove 6-O-sulfate groups of glucosamine residues from heparan sulfate (HS) chains to modulate the Wnt/β-catenin signaling pathway, which plays an important role in both liver carcinogenesis and embryogenesis. Side population (SP) cells are widely identified as stem-like cancer cells and are closely related to carcinoma metastasis, recurrence, and poor patient prognosis. However, the roles of SULF2 in SP cells of hepatomas are unclear, and the underlying mechanism is undefined. Objectives: This study aimed to compare the heterogeneity between SP cells and non-side population (NSP) cells derived from… More >

    Graphic Abstract

    Heterogeneity beyond tumor heterogeneity—SULF2 involvement in Wnt/β-catenin signaling activation in a heterogeneous side population of liver cancer cells

  • Open Access

    ARTICLE

    Blue LED promotes the chemosensitivity of human hepatoma to Sorafenib by inducing DNA damage

    TONG WANG, JINHUAN HONG, JIAJIE XIE, QIAN LIU, JINRUI YUE, XUTING HE, SHIYU GE, TAO LI, GUOXIN LIU, BENZHI CAI, LINQIANG LI, YE YUAN
    BIOCELL, Vol.47, No.8, pp. 1811-1820, 2023, DOI:10.32604/biocell.2023.029120
    (This article belongs to the Special Issue: Application of Deep Learning in Cancer)
    Abstract Background: Phototherapies based on sunlight, infrared, ultraviolet, visible, and laser-based treatments present advantages like high curative effects, small invasion, and negligible adverse reactions in cancer treatment. We aimed to explore the potential therapeutic effects of blue light emitting diode (LED) in human hepatoma cells and decipher the underlying cellular and molecular mechanisms. Methods: Wound healing and transwell assays were employed to probe the inhibition of the invasion and migration of hepatocellular carcinoma cells in the presence of blue LED. The sphere-forming test was used to evaluate the effect of LED blue light irradiation on cancer… More >

    Graphic Abstract

    Blue LED promotes the chemosensitivity of human hepatoma to Sorafenib by inducing DNA damage

  • 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 the 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… More >

  • 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 the 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… More >

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