Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives
share announcement

Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 10181

Special Issue Editors

Dr. Catia Pesquita

E-Mail Website
Guest Editor
Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Interests: ontologies; semantic web; bioinformatics; ontology matching; semantic similarity
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Schlicker

E-Mail Website
Guest Editor
Research and Development Pharmaceuticals, Bayer AG, Berlin Area, 13353 Berlin, Germany
Interests: biomarker development in oncology; DNA damage response; semantic data integration; biomedical ontologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer research relies on a large number of diverse datasets generated by different omics technologies. Electronic health record data, which are gathered at the point of care, are increasingly utilized in pre-clinical and clinical research as well as health management and analyzed in unison with genomic data.

The curation, management, and analysis of these data present unique challenges arising from data heterogeneity, complexity, and size. Artificial intelligence techniques are being increasingly adopted to address these issues, both at the level of knowledge representation, with ontologies and knowledge graphs playing a central role, and at the data analysis level, with sophisticated machine learning approaches that tackle data complexity challenges. Moreover, the integration of knowledge representation with data analytics and machine learning is becoming an increasingly hot topic due to its potential to support explainability and promote multidisciplinary cancer research efforts.

This Special Issue will focus on the challenges afforded by the size, complexity, and heterogeneity of data in cancer research and care, with a focus on artificial intelligence both from the knowledge representation and data perspectives. This includes, but is not limited to, ontology development and evolution, genomic data analysis, ontology-based machine learning and artificial intelligence, semantic data integration using knowledge graphs and other methods, machine learning for network data, machine learning for complex data, use of artificial intelligence in translational medicine, and clinical decision support.

Dr. Catia Pesquita
Dr. Andreas Schlicker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • knowledge representation
  • ontologies
  • knowledge graphs
  • semantic data integration
  • machine learning for complex data

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 5208 KiB  
Article
Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis
by Zeenat Mirza, Md Shahid Ansari, Md Shahid Iqbal, Nesar Ahmad, Nofe Alganmi, Haneen Banjar, Mohammed H. Al-Qahtani and Sajjad Karim
Cancers 2023, 15(12), 3237; https://doi.org/10.3390/cancers15123237 - 18 Jun 2023
Cited by 3 | Viewed by 3291
Abstract
Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed [...] Read more.
Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
►▼ Show Figures

Figure 1

17 pages, 1103 KiB  
Article
Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection
by Riaz Ahmad, Muhammad Awais, Nabeela Kausar, Usman Tariq, Jae-Hyuk Cha and Jamel Balili
Cancers 2023, 15(9), 2507; https://doi.org/10.3390/cancers15092507 - 27 Apr 2023
Cited by 4 | Viewed by 1765
Abstract
Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step [...] Read more.
Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
►▼ Show Figures

Figure 1

17 pages, 4826 KiB  
Article
Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis
by Hanan Abdullah Mengash, Mohammad Alamgeer, Mashael Maashi, Mahmoud Othman, Manar Ahmed Hamza, Sara Saadeldeen Ibrahim, Abu Sarwar Zamani and Ishfaq Yaseen
Cancers 2023, 15(5), 1591; https://doi.org/10.3390/cancers15051591 - 03 Mar 2023
Cited by 7 | Viewed by 1758
Abstract
Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the [...] Read more.
Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator’s algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
►▼ Show Figures

Figure 1

24 pages, 2480 KiB  
Article
Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia
by Nidhi Mehra, Armon Varmeziar, Xinyu Chen, Olivia Kronick, Rachel Fisher, Vamsi Kota and Cassie S. Mitchell
Cancers 2022, 14(19), 4686; https://doi.org/10.3390/cancers14194686 - 26 Sep 2022
Cited by 9 | Viewed by 2560
Abstract
Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI [...] Read more.
Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI abstracts separated TKIs by significant (p < 0.05) AE type: gastrointestinal (bosutinib); edema (imatinib); pulmonary (dasatinib); diabetes (nilotinib); cardiovascular (ponatinib). Next, we propose a novel cross-domain text mining method utilizing a knowledge graph, link prediction, and hub node network analysis to predict new relationships. Cross-domain text mining of 30+ million articles via SemNet predicted and ranked known and novel TKI AEs. Three physiology-based tiers were formed using unsupervised rank aggregation feature importance. Tier 1 ranked in the top 1%: hematology (anemia, neutropenia, thrombocytopenia, hypocellular marrow); glucose (diabetes, insulin resistance, metabolic syndrome); iron (deficiency, overload, metabolism), cardiovascular (hypertension, heart failure, vascular dilation); thyroid (hypothyroidism, hyperthyroidism, parathyroid). Tier 2 ranked in the top 5%: inflammation (chronic inflammatory disorder, autoimmune, periodontitis); kidney (glomerulonephritis, glomerulopathy, toxic nephropathy). Tier 3 ranked in the top 10%: gastrointestinal (bowel regulation, hepatitis, pancreatitis); neuromuscular (autonomia, neuropathy, muscle pain); others (secondary cancers, vitamin deficiency, edema). Results suggest proactive TKI patient AE surveillance levels: regular surveillance for tier 1, infrequent surveillance for tier 2, and symptom-based surveillance for tier 3. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop