A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Le, Nguyen Quoc Khanh; Thi, Duyen; Hung, Truong Nguyen Khanh; Lam, Luu Ho Thanh; Lin, Chih-Min; Nguyen, Ngan Thi Kim

doi:10.3390/ijms21239070

Cited by 52 publications

(39 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, predictive accuracy of 92.5% for EMCI vs. CN and 93% for AD vs. CN is high, especially when considering the complexity of the schemes shown in this table (e.g., a larger number of features, a higher number of architectural parameters, and so on). Further performance improvement can be potentially achieved with SVM and CNN classifiers by optimization of architectures, as demonstrated in other works in the literature [ 29 , 32 ]; a study of this type is included in our immediate plans.…”

Section: Discussionmentioning

confidence: 94%

See 1 more Smart Citation

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Herzog

Magoulas

2021

Sensors

View full text Add to dashboard Cite

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

show abstract

Section: Discussionmentioning

confidence: 94%

“…Another popular method for the improvement of classification performance is the implementation of classifier ensembles [ 29 ]. They combine multiple meta-algorithms in one predictive model in order to minimize error and enhance predictive accuracy.…”

Section: Background Of the Studymentioning

confidence: 99%

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Herzog

Magoulas

2021

Sensors

View full text Add to dashboard Cite

show abstract

“… a : Data obtained from Reference [ 8 ]; b : Data obtained from References [ 37 , 38 ]; c : Data obtained from Reference [ 17 ]; d : Data obtained from References [ 39 , 40 ]; --: Not available or not reported in paper. …”

Section: Resultsmentioning

confidence: 99%

“…In this study, four routinely used evaluation criteria were applied to examine the overall performance of the proposed method: overall accuracy ( ACC ), sensitivity ( Sen ), specificity ( Spe ), and Matthews correlation coefficient ( MCC ). These evaluation criteria are commonly applied in bioinformatics research [ 38 , 40 ] to reveal classification performance. The definitions of these criteria are as follows:

where TP , TN , FP , and FN represent the number of true positive instances, true negative instances, false positive instances, and false negative instances, respectively.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

Song

Liu

Jiang

et al. 2021

IJMS

View full text Add to dashboard Cite

Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein–ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.

show abstract

“…The advent of Machine Learning (ML) and Deep Learning (DL) approaches have created a path towards solving previously challenging unsolved problems in biology and chemistry [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Various reviews summarize the application of ML/DL in drug design and discovery [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Verma

Trozzi

et al. 2021

IJMS

View full text Add to dashboard Cite

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

show abstract

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Cited by 52 publications

References 53 publications

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Contact Info

Product

Resources

About