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Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives
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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: closed (15 August 2024) | Viewed by 15018

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 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

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Published Papers (7 papers)

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Research

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18 pages, 3432 KiB  
Article
Deep Neural Network Integrated into Network-Based Stratification (D3NS): A Method to Uncover Cancer Subtypes from Somatic Mutations
by Matteo Valerio, Alessandro Inno, Alberto Zambelli, Laura Cortesi, Domenica Lorusso, Valeria Viassolo, Matteo Verzè, Fabrizio Nicolis and Stefania Gori
Cancers 2024, 16(16), 2845; https://doi.org/10.3390/cancers16162845 - 14 Aug 2024
Viewed by 796
Abstract
(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method [...] Read more.
(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
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15 pages, 1909 KiB  
Article
Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features
by Domiziana Santucci, Raffaele Ragone, Elva Vergantino, Federica Vaccarino, Francesco Esperto, Francesco Prata, Roberto Mario Scarpa, Rocco Papalia, Bruno Beomonte Zobel, Francesco Rosario Grasso and Eliodoro Faiella
Cancers 2024, 16(15), 2731; https://doi.org/10.3390/cancers16152731 - 31 Jul 2024
Viewed by 592
Abstract
PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS [...] Read more.
PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
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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 8 | Viewed by 4334
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)
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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 5 | Viewed by 2260
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)
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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 - 3 Mar 2023
Cited by 14 | Viewed by 2139
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)
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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 12 | Viewed by 2930
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)
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Review

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18 pages, 763 KiB  
Review
Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion
by Navid Sobhani, Alberto D’Angelo, Matteo Pittacolo, Giuseppina Mondani and Daniele Generali
Cancers 2024, 16(17), 3089; https://doi.org/10.3390/cancers16173089 - 5 Sep 2024
Viewed by 679
Abstract
The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the [...] Read more.
The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives)
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