Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

Uzma,; Al-Obeidat, Feras; Tubaishat, Abdallah; Shah, Babar; Halim, Zahid

doi:10.1007/s00521-020-05101-4

Cited by 27 publications

(11 citation statements)

References 49 publications

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“…Pseudo-Guided Multi-Objective Genetic Algorithm (PGMOGA) proposed that reconstitutes pathways by assigning orientation weighted network edges (Iqbal & Halim, 2020). A gene encoder presented that incorporates twostage feature selection with an unsupervised type for the classification of cancer samples (Al-Obeidat et al, 2020). There is a requirement of finding the correct DNA sequence to get the desired information about the genetic makeup of an organism.…”

Section: Anemia and Sickle Cell Detectionmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

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Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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Section: Anemia and Sickle Cell Detectionmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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“…Zhang et al [51,52] introduce a parallel architecture of a convolutional neural network and a recurrent neural network. The joint output of both the networks is then fed to an autoencoder layer [32,43] to learn a compressed and distributed representation of EEG features. For classification, Extreme Gradient Boosting (XGBoost) classifier is used.…”

Section: Past Work Summarymentioning

confidence: 99%

Imagined character recognition through EEG signals using deep convolutional neural network

Ullah

Halim

2021

Med Biol Eng Comput

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Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user's intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)-based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets.

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“…In healthcare area, GA was utilized to improve the classification models by finding the optimum feature subsets for gene encoder, hepatitis prediction, and COVID-19 patients' detection. Uzma et al (2020) proposed gene encoder based on GA to evaluate the feature subset and three machine learning models such as support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) were used to classify the cancer samples [29]. The experiments on six benchmark datasets revealed that GA-based feature selection improved the performance of all models used in their study.…”

Section: Extreme Gradient Boosting (Xgboost) and Genetic Algorithms (Ga)mentioning

confidence: 99%

“…Genetic algorithms (GAs) have been previously used for feature selection and showed significant results for selecting the best feature sets [24,25]. In the health arena, GA can highly improve the performance of models for emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activity recognition and classification [28], gene encoder [29], hepatitis prediction [30], and COVID-19 patient detection [31].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Feature selection is used to reduce the dimensionality of data [20,21] and in medical diagnosis, is used to identify most significant features related to disease [22,23]. Genetic algorithm (GA) is a feature selection method that has been used to find the best feature subsets [24,25] and shown significant advantage for improving the performance of emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activities regarding recognition and classification [28], gene encoder [29], hepatitis prediction [30], and coronavirus disease (COVID-19) patient detection [31]. A previous study revealed that a combination of GA and XGBoost has improved the model performance [32].…”

Section: Introductionmentioning

confidence: 99%

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

Cited by 27 publications

References 49 publications

A review of microscopic analysis of blood cells for disease detection with AI perspective

A review of microscopic analysis of blood cells for disease detection with AI perspective

Imagined character recognition through EEG signals using deep convolutional neural network

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

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