Computational Approaches: A New Frontier in Cancer Research DOI
Shubham Srivastava, Pushpendra Kumar Jain

Combinatorial Chemistry & High Throughput Screening, Год журнала: 2023, Номер 27(13), С. 1861 - 1876

Опубликована: Ноя. 30, 2023

Abstract: Cancer is a broad category of disease that can start in virtually any organ or tissue the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to most recent statistics, cancer will be cause 10 million deaths worldwide 2020, accounting for one death out every six worldwide. The typical approach used anti-cancer research highly time-consuming expensive, outcomes are not particularly encouraging. Computational techniques have been employed advance our understanding. Recent years seen significant exceptional impact on anticancer due rapid development computational tools novel drug discovery, design, genetic studies, genome characterization, imaging detection, radiotherapy, metabolomics, therapeutic approaches. In this paper, we examined various subfields contemporary techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, QSAR, their applications study cancer.

Язык: Английский

A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM DOI Creative Commons
Sajid Naveed, Mujtaba Husnain

Intelligence-Based Medicine, Год журнала: 2025, Номер unknown, С. 100206 - 100206

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications DOI

Kaustubh Kasture,

Pravin Shende

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(8), С. 4667 - 4685

Опубликована: Июнь 13, 2023

Язык: Английский

Процитировано

14
Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier DOI Creative Commons
Sneha S. Nair, V. N. Meena Devi, Saju Bhasi

и другие.

Heliyon, Год журнала: 2024, Номер 10(7), С. e29032 - e29032

Опубликована: Апрель 1, 2024

Medical image segmentation is a vital yet difficult job because of the multimodality acquired images. It to locate polluted area before it spreads.

Язык: Английский

Процитировано

6
The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide DOI Creative Commons
Amin Zadeh Shirazi,

Morteza Tofighi,

Alireza Gharavi

и другие.

Technology in Cancer Research & Treatment, Год журнала: 2024, Номер 23

Опубликована: Янв. 1, 2024

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, Deep Learning oncology, explaining key concepts algorithms (like SVM, Naïve Bayes, CNN) a clear, accessible manner. It aims to make advancements understandable broad audience, focusing on their application diagnosing, classifying, predicting various types, thereby underlining AI's potential better outcomes. Moreover, we present tabular summary most significant advances from literature, offering time-saving resource for readers grasp each study's main contributions. The remarkable benefits AI-powered underscore advancing research clinical practice. is valuable researchers clinicians interested transformative implications care.

Язык: Английский

Процитировано

4
Recent Advancement in Bioinformatics DOI
Yogesh Kumar Sharma, Leena Arya,

Smitha

и другие.

Опубликована: Янв. 13, 2025

Язык: Английский

Процитировано

0
Artificial Intelligence in Oral Health DOI
Harisankar Binod,

T. Shithij,

Antonín Tichý

и другие.

Опубликована: Янв. 1, 2025

Процитировано

0
Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory study DOI Creative Commons
Jeremiah Isuwa, Mohammed Abdullahi, Yusuf Sahabi Ali

и другие.

Egyptian Informatics Journal, Год журнала: 2023, Номер 24(4), С. 100416 - 100416

Опубликована: Ноя. 18, 2023

Microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are type biomarker that holds great importance understanding various types tumors, including brain, lung, breast cancers. However, significant portion these cancer genes not directly target disease, which can lead to challenges during analysis, such as increased computational complexity, poor generalization, decreased classification accuracy, among others. To address this issue, range techniques algorithms have been developed optimize selection most relevant subset genes. One highly effective approach handle challenge is use Swarm Intelligent (SI) algorithms, known their efficiency effectiveness global search agents. In paper, we present two distinct but related sections. First, conduct survey current literature from 2019 present, on SI optimizing an optimal Secondly, based analysis findings first part, presentation experimental study evaluates efficacy four classical - Particle Optimization (PSO), Salp (SSA), Firefly Algorithm (FA), Cuckoo Search (CS) – three different datasets. For study, used Chi-square, Mutual Information, ANOVA filter methods individually select 100, 200, 500 identified We then passed input each algorithms. The results indicate diverse filter-wrapper combinations effectively selecting across

Язык: Английский

Процитировано

4
Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach DOI Creative Commons
Gerardo Alfonso, Raquel Castillo

Medicina, Год журнала: 2023, Номер 59(7), С. 1218 - 1218

Опубликована: Июнь 28, 2023

Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life patient. The rather heterogeneous different evolution among patients. A machine learning approach followed this paper to identify potential genes that are related IBD. This done by following Monte Carlo simulation approach. In total, 23 techniques were tested (in addition base level obtained using artificial neural networks). best model identified 74 selected algorithm as being potentially involved IBD seems be polygenic illness, environmental factors might play important role. Following approach, it was possible obtain classification accuracy 84.2% differentiating between patients and control cases large cohort 2490 total cases. sensitivity specificity 82.6% 84.4%, respectively. It also distinguish two main types IBD: (1) Crohn's (2) ulcerative colitis.

Язык: Английский

Процитировано

2
Modeling of proteins DOI Creative Commons
Gerardo Alfonso

Опубликована: Ноя. 10, 2023

The mapping of categorical variables into numerical values is common in machine learning classification problems.This type frequently performed a relatively arbitrary manner.We present series four assumptions (tested numerically) regarding these mappings the context protein using amino acid information.This assumption involves problems without need to use approaches such as natural language process (NLP).The first three relate equivalent mappings, and fourth comparable proposed eigenvalue-based matrix representation chain.These were tested across range 23 different algorithms.It shown that simulations are consistent with presented assumptions, translation permutations, eigenvalue approach generates classifications statistically not from base case or have higher mean while at same time providing some advantages having fixed predetermined dimensions regardless size analyzed protein.This generated an accuracy 83.25%.An optimization algorithm also selects appropriate number neurons artificial neural network applied above-mentioned problem, achieving 85.02%.The model includes quadratic penalty function decrease chances overfitting.

Язык: Английский

Процитировано

0
Computational Approaches: A New Frontier in Cancer Research DOI
Shubham Srivastava, Pushpendra Kumar Jain

Combinatorial Chemistry & High Throughput Screening, Год журнала: 2023, Номер 27(13), С. 1861 - 1876

Опубликована: Ноя. 30, 2023

Abstract: Cancer is a broad category of disease that can start in virtually any organ or tissue the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to most recent statistics, cancer will be cause 10 million deaths worldwide 2020, accounting for one death out every six worldwide. The typical approach used anti-cancer research highly time-consuming expensive, outcomes are not particularly encouraging. Computational techniques have been employed advance our understanding. Recent years seen significant exceptional impact on anticancer due rapid development computational tools novel drug discovery, design, genetic studies, genome characterization, imaging detection, radiotherapy, metabolomics, therapeutic approaches. In this paper, we examined various subfields contemporary techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, QSAR, their applications study cancer.

Язык: Английский

Процитировано

0