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Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment

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Abstract

Machine learning is an established data interpretation tool for the development of processing, extracting and extrapolating evocative results from complex data sets. Data-based and computer-aided cancer research in patients expands at a rapid pace and presents a growing landscape of potential for Machine Learning methodologies, driven by the growing need for personalization of medical procedures. Previous research on artificial neural networks displays remarkable improvements in data mining tools and superior computational performance in prediction and diagnostics of cancer. For data mining, the article initially reviews machine-learning tools available for detection, susceptibility, reoccurrence and prediction of cancer prognosis. This article summarizes major challenges and problem-solving methods with examples of tools and brief description of algorithms used to improve the efficiency of cancer treatment and the development of personalized medicine and treatment for diverse types of cancer-based on genomic and protein data.

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Data Availability

Data sharing is not applicable to this article as no databases were generated or analyzed during the current study.

Abbreviations

ML:

Machine learning

ANN:

Artificial Neural Networks

DTs:

Decision Trees

CRT:

Cathode-ray tube

CNN:

Convolutional Neural Network

miRNA:

MicroRNAs

RT:

Radiotherapy

SVM:

Support Vector Machine

BRAF:

v-raf murine sarcoma viral oncogene homolog B1

TP53:

tumor protein p53

CREBBP:

Cyclic adenosine monophosphate Response Element Binding protein

MYC:

MYC Proto-Oncogene, BHLH Transcription Factor

FlLpF:

fast focal Laplacian filtering

HSV:

Herpes Simplex

TCGA:

The Cancer Genome Atlas

ICGA:

Indocyanine green angiography

CFEs:

cancer functional event

NLCLCs:

Non-small cell lung cancer

DEMETER:

Digital Transformation of the European Agrifood sector

PPWD1:

Peptidylprolyl Isomerase Domain And WD Repeat Containing 1

NXF1:

Nuclear export factor 1

LRR:

leucine-rich repeat

PSHG:

polarization-dependent second-harmonic generation

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  1. Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’S NMIMS, V. L. Mehta Road, Vile Parle (W), Mumbai, India

    Maitri Bhatt & Pravin Shende

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  1. Maitri Bhatt
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  2. Pravin Shende
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Correspondence to Pravin Shende.

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Bhatt, M., Shende, P. Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment. Arch Computat Methods Eng 30, 2777–2792 (2023). https://doi.org/10.1007/s11831-023-09886-0

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