Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Vaz, Joel Markus; Balaji, S.

doi:10.1007/s11030-021-10225-3

Cited by 36 publications

(23 citation statements)

References 99 publications

(105 reference statements)

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“…CNN is a valuable tool in the analysis of biological data and is a type of DL algorithm inspired by the natural visual perception mechanism in biology . LeCun et al proposed LeNet-5 in 1998 for standard handwritten character recognition .…”

Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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“…Therefore, as shown in Figure 1, CNNs technique is proposed to be implemented in identifying and classifying between lncRNAs and mRNAs being expressed in human DCs. We intend to explore the capability of CNNs to extract information from one-dimensional biological sequences data as discussed by [36], [37].…”

Section: Convulational Neural Networkmentioning

confidence: 99%

A framework for predicting lncRNAs expression in human dendritic cells in response to M. tuberculosis infection

Aplop

Mohamad

2023

Bulletin EEI

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Tuberculosis (TB) is an air-borne infectious diseases caused by M. tuberculosis bacteria that primarily affects human lungs. Existing vaccine does not work well due to the evolvement and latent movement of this bacteria. Developing an effective vaccine to combat Tuberculosis is very difficult as the interaction between the bacteria and human immune system is not fully understood. With recent advancement of transcriptome profiling analysis, long noncoding ribonukleat acids (lncRNAs) are found to be widely expressed in immune system. However, the role of lncRNAs is still not been widely explored in understanding human immune response to TB infection. In this paper, we propose a general framework for predicting lncRNAs being expressed in human dendritic cells. By incorporating deep learning method with RNA-seq data analysis, we intend to identify and characterize the lncRNAs found in dendritic cells from two groups of TB resistant patients through their RNA-seq expression data.

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“…Apart from the simple feed-forward model discussed above, there are other specialized architectures of neural networks suited for specific tasks. For instance, convolutional neural networks (CNNs) have a grid-like topology and are well suited to process two or three-dimensional inputs such as images [ 31 ]. CNNs are designed to capture spatial context and learn correlations between local features, due to which they yield superior performance on image tasks, such as the classification of breast lesions in a screening mammogram as probable malignant or benign (See Figure 1 c).…”

Section: How Does Artificial Intelligence Work?mentioning

confidence: 99%

“…DL radiomics use CNNs, in which the model learns in a cascading fashion without any prior description of features and requires a large amount of data in the learning process. The cascading technique processes data to obtain useful information, removes redundancies, and prevents overfitting [ 27 , 31 , 98 ].…”

Section: Central Nervous System Cancersmentioning

confidence: 99%

Advancements in Oncology with Artificial Intelligence—A Review Article

Vobugari

Raja

Sethi

et al. 2022

Cancers

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Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

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Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Cited by 36 publications

References 99 publications

Machine Learning Methods for Small Data Challenges in Molecular Science

Machine Learning Methods for Small Data Challenges in Molecular Science

A framework for predicting lncRNAs expression in human dendritic cells in response to M. tuberculosis infection

Advancements in Oncology with Artificial Intelligence—A Review Article

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