Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture

Chakraborty, Rajkumar; Hasija, Yasha

doi:10.1109/tcbb.2019.2936186

Cited by 12 publications

(7 citation statements)

References 37 publications

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“…To further improve classification performance, hybrid DL (HDL) and ensemble DL (EDL) models have been proposed 60,61 . These models leverage the strengths of multiple DL architectures [62][63][64] .…”

Section: Symbolsmentioning

confidence: 99%

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

Singh,

Khanna,

Rout

et al. 2024

Sci Rep

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Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint’s GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

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Section: Symbolsmentioning

confidence: 99%

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides

Singh,

Khanna,

Rout

et al. 2024

Sci Rep

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“…The selection of the final ensemble is detailed in Table 3, which outlines the ensemble assessment. From Table 3, we discern that the performance enhancements are notable in the case of ensembles utilizing algorithms like adaptive boosting [40], random forest (RF) [41], support vector regressor (SVR) [29], long short-term memory (LSTM) [20], gated recurrent unit (GRU) [27], and Stacked LSTM [31]. Consequently, the ensemble chosen as our ultimate selection encompasses the adaptive boosting, RF, SVR, LSTM, GRU, and Stacked LSTM algorithms.…”

Section: Ensemble Learning Technique (Elt)mentioning

confidence: 99%

“…The advantages and disadvantages of our proposed PDD-ET model and the existing models are outlined in Tables 1 and 2. Stacked-LSTM [31] (i) Its proficiency in capturing and learning long-term dependencies within sequential data. (ii) It can effectively handle complex temporal patterns in time series data related to PD symptoms.…”

Section: Introductionmentioning

confidence: 99%

PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset

Chatterjee,

Kumar,

Bandyopadhyay

et al. 2023

Information

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Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively.

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“…AI and machine learning can be used to analyze large datasets, such as genomic data, to identify patterns and trends relevant to the understanding and treatment of infectious diseases [ 9 , 10 , 11 , 12 ]. For example, machine learning algorithms have been utilized to identify potential drug targets for SARS-CoV-2, which causes COVID-19 [ 13 , 14 ].…”

Section: An Opinionmentioning

confidence: 99%