A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis

Gunavathi, C.; Sivasubramanian, K.; Keerthika, P.; Paramasivam, C

doi:10.1016/j.matpr.2020.10.263

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…Because of the limited number of studies available on the topic, and although it has been studied, articles have not been discarded because of the quality of the journal in which they were published (i.e., JIF), and this may have influenced, in some way, the results of this review. In addition, to reduce variability in study methodology and facilitate the analysis, we have only focused on those studies using DNA-based sequencing, not including other NGS methodologies such as RNA-seq, which are widely used in conjunction with AI/ML methodologies [45][46][47] .…”

Section: Limitationsmentioning

confidence: 99%

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Roman‐Naranjo

Parra-Perez

López-Escámez

2023

Preprint

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Background: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases. Methods: We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis. Findings: Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations. Conclusions: ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases. Keywords: artificial intelligence, rare diseases, precision medicine, rare variants, DNA-sequencing, genomics

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Section: Limitationsmentioning

confidence: 99%

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Roman‐Naranjo

Parra-Perez

López-Escámez

2023

Preprint

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“…Scholars have begun to study the depth of CNN, and the network architecture has also begun to develop in a deeper and deeper. If there are more convolutional layers, CNN can easily detect complex objects or patterns [44,45]. By increasing the depth of the CNN, the non-linearly increased objective function can be better approximated to get better results [46].…”

Section: Improvement Of Cnn Architecturementioning

confidence: 99%

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Ming

Cao

Zhang³

et al. 2021

IEEE Access

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Based on the rapid development of semiconductors, integrated circuits and the Internet. 3C products such as computers, tablets, mobile phones and smart TVs have become an indispensable part of people's lives. With the prosperity and development of the 3C product market, the demand for the quality of display panels and related detection technologies are increasing. As the iconic network of deep learning, has been extensively studied in the field of image recognition and defect detection. Based on the development of CNN, this article summarizes the defect detection method of 3C products by CNN with different depths. First, we reviewed the origin of CNN and its structural components, then introduced the upgrade and improvement of important components, and finally introduced and compared the applications of CNN with different depths in defect detection. Through the comparison and summary of the effect of defect detection, we analyze the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.

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“…Bu bakımında ESA görsel nesne analizi, tanıma ve sınıflandırma, ses tanıma ve doğal dil işleme gibi alanlarda yaygın olarak kullanılmaktadır. ESA, klasik sinir ağlarından faklı olarak evirişim öznitelikleri çıkarma ve sınıflama özelliklerine sahiptir (Gunavathi et al, 2020). Şekil 1'deki ESA genel mimarisinde görüldüğü üzere ESA'lar birçok evrişim, aktivasyon, havuzlama, tam bağlı ve softmax katmanlarından oluşmaktadır.…”

Section: Evrişimli Sinir Ağıunclassified

Derin Öğrenme ile Bitki Hastalıklarının Tespiti

Aslan¹

2021

European Journal of Science and Technology

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ÖzTarım ürünleri, dünyadaki canlıların beslenme ihtiyaçlarının karşılanması bakımından oldukça önemlidir. Dünya nüfusundaki hızlı artış tarımsal ürünlerde verimliğin arttırılmasını zorunlu hale getirmektedir. Sınırlı tarım alanlarında ürün verimliliğinin sağlanabilmesi bitkilerde görülebilecek hastalıklarının etkili bir şekilde ve zamanında tespiti oldukça önemlidir. Özellikle bazı meyve ağaçlarının kısa ömürlü olması bu ağaçlardaki hastalıkların doğru, zamanında ve hızlı bir şekilde tespitini daha önemli hale getirmektedir. Son zamanlarda görüntü işlemede yaygın olarak kullanılan derin öğrenme, tarımsal faaliyetlerde etkili uygulamalar sunmaktadır. Bu çalışmada, şeftali ağacı hastalıklarının tespiti için evrişimli sinir ağ yöntemi önerilmiştir. Önerilen yöntemde, daha önceden eğitilmiş AlexNet modeli ile şeftali ağaçlarında görülen monilya ve koşnili hastalık tespiti yapılmıştır. Deneysel çalışmalarda, TRB1 bölgesinden alınan gerçek hastalık görüntülerinden oluşan veri seti ile gerçekleştirildi. Yapılan deneysel çalışmalarda %99,30 doğruluk oranında hastalık tespiti yapılmıştır. Mevcut çalışmalardan %1,44 daha yüksek doğruluk oranına sağlandı.

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A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis

Cited by 20 publications

References 12 publications

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Derin Öğrenme ile Bitki Hastalıklarının Tespiti

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