Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

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“…Yanagisawa et al . [ 95 ] constructed a CNN model to predict the efficiency of antitumor drugs at the single-cell level.…”

Section: Ai In Cancer Clinical Research: Methods and Applicationmentioning

confidence: 99%

Artificial intelligence in clinical research of cancers

Shao

Dai

et al. 2021

Briefings in Bioinformatics

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“…Nonlinear functions are used to filter features via convolutions. The dimensionality can then be reduced via pooling [19].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Drug Sentiment Analysis using Machine Learning Classifiers

Uddin¹,

Hafiz²,

Hossain³

et al. 2022

IJACSA

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In recent times, one of the most emerging subdimensions of natural language processing is sentiment analysis which refers to analyzing opinion on a particular subject from plain text. Drug sentiment analysis has become very significant in present times as classifying medicines based on their effectiveness through analyzing reviews from users can assist potential future consumers in gaining knowledge and making better decisions about a particular drug. The objective of this proposed research is to measure the effectiveness level of a particular drug. Currently most of the text mining researches are based on unsupervised machine learning methods to cluster data. When supervised learning methods are used for text mining, the usual primary concern is to classify the data into two classes. Lack of technical terms in similar datasets make the categorization even more challenging. The proposed research focuses on finding out the keywords through tokenization and lemmatization so that better accuracy can be achieved for categorizing the drugs based on their effectiveness using different algorithms. Such categorization can be instrumental for treating illness as well as improve one's health and well-being. Four machine learning algorithms have been applied for binary classification and one for multiclass classification on the drug review dataset acquired from the UCI machine learning repository. The machine learning algorithms used for binary classification are naive Bayes classifier, random forest, support vector classifier (SVC), and multilayer perceptron; among these machine learning algorithms, linear SVC was used for multiclass classification. Results obtained from these four classifier algorithms have been analyzed to evaluate their performances. The random forest has been proven to have the best performance among these four algorithms. However, multiclass classification was found to have low performance when applied to natural language processing. On the contrary, the applied linear SVC algorithm performed better for class 2 with AUC 0.82 in this research.

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“…Yanagisawa et al also managed to build a CNN that examines circulating tumor cells, which are indicative of malignancy. They could evaluate, with up to 80% accuracy, the efficacy of anticancer drugs in single cancer cells [249]. This is among the first ML methods to accurately identify cell characteristics at the single-cell level, which is much more challenging than the identification of properties at the cell population level.…”

Section: Cancermentioning

confidence: 99%

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi

2021

Future Drug. Discov.

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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

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Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

Cited by 13 publications

References 16 publications

Artificial intelligence in clinical research of cancers

Artificial intelligence in clinical research of cancers

Drug Sentiment Analysis using Machine Learning Classifiers

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

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