Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction

2020 ◽  
Vol 20 (21) ◽  
pp. 1858-1867
Author(s):  
Xian Tan ◽  
Yang Yu ◽  
Kaiwen Duan ◽  
Jingbo Zhang ◽  
Pingping Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
Anticancer Drug ◽  
Drug Screening ◽  
Drug Sensitivity ◽  
Data Sources ◽  
Advantages And Disadvantages ◽  
Computational Performance ◽  
Drug Sensitivity Prediction

Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5277
Author(s):  
Lauv Patel ◽  
Tripti Shukla ◽  
Xiuzhen Huang ◽  
David W. Ussery ◽  
Shanzhi Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Drug Candidates ◽  
Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

scholarly journals Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application

2021 ◽  
Vol 3 ◽  
Author(s):  
Dan Luo ◽  
Wei Zeng ◽  
Jinlong Chen ◽  
Wei Tang
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Data Sources ◽  
X Rays ◽  
Advantages And Disadvantages ◽  
Backbone Network ◽  
Active Research

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

scholarly journals Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7527
Author(s):  
Mugdim Bublin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Event Detection ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Efficient System ◽  
Acoustic Sensing ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches.

scholarly journals Representation transfer for differentially private drug sensitivity prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i218-i224
Author(s):  
Teppo Niinimäki ◽  
Mikko A Heikkilä ◽  
Antti Honkela ◽  
Samuel Kaski
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Private Information ◽  
Drug Sensitivity ◽  
Genomic Data ◽  
Representation Learning ◽  
Compact Representation ◽  
Cancer Type ◽  
Learning Methods ◽  
Drug Sensitivity Prediction

Abstract Motivation Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymization strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information. Results We study an approach that uses a large public dataset of similar type to learn a compact representation for differentially private learning. We compare three representation learning methods: variational autoencoders, principal component analysis and random projection. We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction. The experiments demonstrate significant benefit from all representation learning methods with variational autoencoders providing the most accurate predictions most often. Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction. Availability and implementation Code used in the experiments is available at https://github.com/DPBayes/dp-representation-transfer.

scholarly journals A Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

2021 ◽  
Author(s):  
Aayush Gupta ◽  
Huan-Xiang Zhou
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Screening ◽  
Drug Target ◽  
Large Scale ◽  
False Positives ◽  
Virtual Drug Screening

Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever increasing sizes of libraries of drug-like compounds, and separating true positives from false positives. Here we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome and a drug target for breast cancer.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

2021 ◽  
Author(s):  
Sethu Arun Kumar ◽  
Thirumoorthy Durai Ananda Kumar ◽  
Narasimha M Beeraka ◽  
Gurubasavaraj Veeranna Pujar ◽  
Manisha Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Data Acquisition ◽  
Data Driven ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

2020 ◽  
Author(s):  
Thomas R. Lane ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Fabio Urbina ◽  
Kimberley M. Zorn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Drug Discovery ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

<p>Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies we have applied multiple machine learning algorithms, modeling metrics and in some cases compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and comparison of our proprietary software Assay Central<sup>TM</sup> with random forest, k-Nearest Neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (3 levels). Model performance <a>was</a> assessed using an array of five-fold cross-validation metrics including area-under-the-curve, F1 score, Cohen’s kappa and Matthews correlation coefficient. <a>Based on ranked normalized scores for the metrics or datasets all methods appeared comparable while the distance from the top indicated Assay Central<sup>TM</sup> and support vector classification were comparable. </a>Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case where minimal tuning was performed of any of the methods. If anything, Assay Central<sup>TM</sup> may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central<sup>TM</sup>performance, but support vector classification seems to be a strong competitor. We also apply Assay Central<sup>TM</sup> to prospective predictions for PXR and hERG to further validate these models. This work currently appears to be the largest comparison of machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors and algorithms, as well as further refining methods for evaluating and comparing models. </p><p><b> </b></p>

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