Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

Journal of Medical Systems, Journal Year: 2024, Volume and Issue: 48(1)

Published: Jan. 9, 2024

Language: Английский

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A two-phase cuckoo search based approach for gene selection and deep learning classification of cancer disease using gene expression data with a novel fitness function

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(28), P. 71721 - 71752

Published: Feb. 6, 2024

Language: Английский

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Graphene Metasurface Based Biosensor for COVID-19 Detection in the Terahertz Regime with Machine Learning Optimization using K-Nearest Neighbours Regression

Plasmonics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 2, 2024

Language: Английский

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Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights

Cancer Immunology Immunotherapy, Journal Year: 2024, Volume and Issue: 73(12)

Published: Oct. 9, 2024

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various types. Conventional feature selection methods often struggle effectively navigate vast solution space while maintaining high predictive accuracy. In response these challenges, we introduce novel approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed enhance performance classification tasks. Our proposed framework not only improves accuracy but also offers valuable insights into underlying biological mechanisms driving progression. Through comprehensive experiments conducted on real-world datasets, including Central Nervous System (CNS), Leukemia, Breast, Ovarian cancers, demonstrate efficacy our method identifying smaller subset unique genes. This results significantly improved efficiency When compared popular classifiers such as Support Vector Machine, K-Nearest Neighbor, Naive Bayes, consistently outperforms models terms both F-measure metrics. For instance, achieved an 97.24% CNS dataset, 99.14% 95.21% Ovarian, 87.62% Breast cancer, showcasing its robustness effectiveness across different types data. These underline potential RDO-XGBoost promising for analysis, offering enhanced insights.

Language: Английский

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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 13, 2025

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise cancer cases. Despite potential to mitigate adverse effects of by early detection implementation prevention methods, several nations have limited screening facilities. In oncology, use artificial intelligence (AI) represents transformative advancement diagnosis, prognosis, treatment. The AI biomarker discovery improves precision medicine uncovering signatures that essential treatment diseases within vast diverse datasets. Deep learning machine diagnostics two examples technologies changing way biomarkers made finding patterns large datasets making new make it possible deliver accurate effective therapies. Existing gaps include data quality, algorithmic transparency, ethical concerns privacy, among others. methodologies with seeks transform improving patient survival rates through enhanced diagnosis targeted therapy. This commentary aims clarify how is identification novel optimal focused treatment, improved clinical outcomes, while also addressing certain obstacles issues related application oncology. Data from reputable scientific databases such as PubMed, Scopus, ScienceDirect were utilized.

Language: Английский

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Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

International Journal of Imaging Systems and Technology, Journal Year: 2023, Volume and Issue: 34(2)

Published: Dec. 16, 2023

Abstract This study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated can be fatal. Despite advances in AI, remains a challenging task. To address this challenge, we propose novel optimization approach called PSCS combined with deep learning for tumor classification. optimizes classification process by improving Particle Swarm Optimization (PSO) exploitation Cuckoo search (CS) algorithm. Next, classified gene expression data Deep Learning (DL) to identify different groups or classes related particular along technique. The proposed technique DL achieves much better accuracy than other existing and Machine models evaluation matrices such Recall, Precision, F1‐Score, confusion matrix. research contributes AI‐driven diagnosis classification, offering promising solution improved patient outcomes.

Language: Английский

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RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 10, 2024

Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection exacerbated by the dimensionality and complexity gene expression data, which complicates classification process.

Language: Английский

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Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 6, 2024

Language: Английский

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From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

Molecular Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for discovery, is underpinned by big data, transformative force in current era. Omics characterized its heterogeneity enormity, ushered biological biomedical research into data domain. Acknowledging significance integrating diverse omics strata, known as multi-omics studies, researchers delve intricate interrelationships among various layers. review navigates expansive landscape, showcasing tailored assays each layer through genomes to metabolomes. The sheer volume generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging robust tools. These datasets not only refine classification but also enhance diagnostics foster development therapeutic strategies. Through integration high-throughput focuses targeting modeling multiple disease-regulated networks, validating interactions targets, enhancing potential using network pharmacology approaches. Ultimately, this exploration aims illuminate impact era, shaping future research.

Language: Английский

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From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(17), P. 9463 - 9463

Published: Aug. 30, 2024

Colorectal cancer (CRC) represents a significant global health burden, with high incidence and mortality rates worldwide. Recent progress in research highlights the distinct clinical molecular characteristics of colon versus rectal cancers, underscoring tumor location's importance treatment approaches. This article provides comprehensive review our current understanding CRC epidemiology, risk factors, pathogenesis, management strategies. We also present intricate cellular architecture colonic crypts their roles intestinal homeostasis. carcinogenesis multistep processes are described, covering conventional adenoma-carcinoma sequence, alternative serrated pathways, influential Vogelstein model, which proposes sequential

Language: Английский

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