CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data | Paper Citation Record

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This paper cites Outbreak of pneumonia of unknown etiology in wuhan, china: The mystery and the miracle.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Outbreak of pneumonia of unknown etiology in wuhan, china: The mystery and the miracle

Reference 1

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This paper cites A predictive model to explore risk factors for severe covid-19.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data A predictive model to explore risk factors for severe covid-19

Reference 2

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This paper cites Predicting the disease outcome in covid-19 positive patients through machine learning: A retrospective cohort study with brazilian data.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predicting the disease outcome in covid-19 positive patients through machine learning: A retrospective cohort study with brazilian data

Reference 3

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This paper cites Human sars cov-2 spike protein mutations.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Human sars cov-2 spike protein mutations

Reference 4

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This paper cites Sensitivity of two sars-cov-2 variants with spike protein mutations to neutralising antibodies.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Sensitivity of two sars-cov-2 variants with spike protein mutations to neutralising antibodies

Reference 5

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This paper cites Predicting natural evolution in the rbd region of the spike glycoprotein of sars-cov-2 by machine learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predicting natural evolution in the rbd region of the spike glycoprotein of sars-cov-2 by machine learning

Reference 6

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This paper cites Empowering open data sharing for social good: a privacy-aware approach.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Empowering open data sharing for social good: a privacy-aware approach

Reference 7

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This paper cites Gisaid’s role in pandemic response.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Gisaid’s role in pandemic response

Reference 8

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This paper cites Data, disease and diplomacy: Gisaid’s innovative contribution to global health.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Data, disease and diplomacy: Gisaid’s innovative contribution to global health

Reference 9

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This paper cites An innovative ai-based primer design tool for precise and accurate detection of sars-cov-2 variants of concern.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data An innovative ai-based primer design tool for precise and accurate detection of sars-cov-2 variants of concern

Reference 10

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This paper cites Explainable artificial intelligence approaches for covid-19 prognosis prediction using clinical markers.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Explainable artificial intelligence approaches for covid-19 prognosis prediction using clinical markers

Reference 11

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This paper cites Severity prediction for covid-19 patients via recurrent neural networks.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Severity prediction for covid-19 patients via recurrent neural networks

Reference 12

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This paper cites Covid-19 health data prediction: a critical evaluation of cnn-based approaches.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Covid-19 health data prediction: a critical evaluation of cnn-based approaches

Reference 13

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This paper cites Cnn-lstm deep learning based forecasting model for covid-19 infection cases in nigeria, south africa and botswana.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Cnn-lstm deep learning based forecasting model for covid-19 infection cases in nigeria, south africa and botswana

Reference 14

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This paper cites A machine learning model for the prediction of covid-19 severity using rna-seq, clinical, and co-morbidity data.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data A machine learning model for the prediction of covid-19 severity using rna-seq, clinical, and co-morbidity data

Reference 15

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This paper cites Predicting effects of noncoding variants with deep learning-based sequence model.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predicting effects of noncoding variants with deep learning-based sequence model

Reference 16

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This paper cites Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.Methods, 166:4–21, 2019.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.Methods, 166:4–21, 2019

Reference 17

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This paper cites Deep learning in bioinformatics.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Deep learning in bioinformatics

Reference 18

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This paper cites The relation between the divergence of sequence and structure in proteins.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data The relation between the divergence of sequence and structure in proteins

Reference 19

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This paper cites Introduction to Protein Structure.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Introduction to Protein Structure

Reference 20

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This paper cites Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network

Reference 21

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey

Reference 22

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data A high performance hybrid lstm cnn secure architecture for iot environments using deep learning

Reference 23

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This paper cites High rate of mutational events in SARS-CoV-2 genomes across brazilian geographical regions, february 2020 to june 2021.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data High rate of mutational events in SARS-CoV-2 genomes across brazilian geographical regions, february 2020 to june 2021

Reference 24

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This paper cites A simple method for displaying the hydropathic character of a protein.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data A simple method for displaying the hydropathic character of a protein

Reference 25

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Physiology, acid base balance

Reference 26

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Biopython: freely available python tools for computational molecular biology and bioinfor- matics

Reference 27

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This paper cites Prediction of protein antigenic determinants from amino acid sequences.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Prediction of protein antigenic determinants from amino acid sequences

Reference 28

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This paper cites Unraveling the structural and chemical features of biological short hydrogen bonds.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Unraveling the structural and chemical features of biological short hydrogen bonds

Reference 29

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This paper cites Mutation informatics: Sars-cov-2 receptor-binding domain of the spike protein.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Mutation informatics: Sars-cov-2 receptor-binding domain of the spike protein

Reference 30

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Local weighting schemes for protein multiple sequence alignment

Reference 31

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Array programming with numpy

Reference 32

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data pandas-dev/pandas: Pandas 1.0.3

Reference 33

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Low: Training deep neural networks by learning optimal sample weights

Reference 34

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Tensorflow: Large-scale machine learning on heterogeneous systems

Reference 35

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Unresolved cited work

Reference 36

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Optuna: A next-generation hyperparameter optimization framework

Reference 37

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Deep Learning using Rectified Linear Units (ReLU)

Reference 38

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Smote: Synthetic minority over-sampling technique

Reference 39

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Feature selection, l1 vs

Reference 40

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models

Reference 41

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CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Unresolved cited work

Reference 42

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This paper cites Predicting covid-19 disease severity from sars-cov-2 spike protein sequence by mixed effects machine learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predicting covid-19 disease severity from sars-cov-2 spike protein sequence by mixed effects machine learning

Reference 43

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This paper cites Interpretable and predictive deep neural network modeling of the sars-cov-2 spike protein sequence to predict covid-19 disease severity.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Interpretable and predictive deep neural network modeling of the sars-cov-2 spike protein sequence to predict covid-19 disease severity

Reference 44

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This paper cites Predicting the sequence specificities of dna-and rna-binding proteins by deep learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predicting the sequence specificities of dna-and rna-binding proteins by deep learning

Reference 45

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This paper cites Convolutional neural network architectures for predicting dna-protein binding.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Convolutional neural network architectures for predicting dna-protein binding

Reference 46

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This paper cites Deep generative models of genetic variation capture the effects of mutations.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Deep generative models of genetic variation capture the effects of mutations

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:25.413086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 74c40ea7-65ce-4da0-9373-380b10b89753 · outbound

This paper cites Efficient and targeted covid-19 border testing via reinforcement learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Efficient and targeted covid-19 border testing via reinforcement learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:25.244582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd6b547c-126f-4908-a1ea-555a990363cf · outbound

This paper cites Deep learning regularization techniques to genomics data.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Deep learning regularization techniques to genomics data

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:25.092268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a99656e0-abea-4238-ae55-20ebc388ecb0 · outbound

This paper cites Bias in error estimation when using cross-validation for model selection.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Bias in error estimation when using cross-validation for model selection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.942319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cb9d210-4e9c-4ed8-a5f2-950b1f2a53e0 · outbound

This paper cites Early detection of sars-cov-2 p.1 variant in southern brazil and reinfection of the same patient by p.2.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Early detection of sars-cov-2 p.1 variant in southern brazil and reinfection of the same patient by p.2

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.746239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16fcf751-a07a-4780-9bc2-d2ef6dc34dd0 · outbound

This paper cites Genomics and epidemiology of the p.1 sars-cov-2 lineage in manaus, brazil.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Genomics and epidemiology of the p.1 sars-cov-2 lineage in manaus, brazil

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.601620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a353173-0989-455b-b420-6ef994e583a7 · outbound

This paper cites Predominance of the sars-cov-2 lineage p.1 and its sublineage p.1.2 in patients from the metropolitan region of porto alegre, southern brazil in march 2021.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Predominance of the sars-cov-2 lineage p.1 and its sublineage p.1.2 in patients from the metropolitan region of porto alegre, southern brazil in march 2021

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.418356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82505d78-e065-4170-8831-ba4a033b56c0 · outbound

This paper cites Genomic surveillance and sequencing of SARS-CoV-2 across south america.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Genomic surveillance and sequencing of SARS-CoV-2 across south america

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.213752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ae4841b-f56a-450c-9753-39e7d9fbbfe9 · outbound

This paper cites Sars-cov-2 mutations and covid-19 clinical outcome: Mutation global frequency dynamics and structural modulation hold the key.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Sars-cov-2 mutations and covid-19 clinical outcome: Mutation global frequency dynamics and structural modulation hold the key

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:24.027387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f94f0af-82c9-4006-9aeb-aa9c94d74bcc · outbound

This paper cites Sars-cov-2 variants and clinical outcomes: A systematic review.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Sars-cov-2 variants and clinical outcomes: A systematic review

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:23.853847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 41b12812-6a15-46a8-98ea-5ee3b9bcd7d7 · outbound

This paper cites A comprehensive analysis of structural and functional changes induced by sars-cov-2 spike protein mutations.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data A comprehensive analysis of structural and functional changes induced by sars-cov-2 spike protein mutations

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:23.677164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75acdccf-660e-4a77-9e0c-790f4da85138 · outbound

This paper cites Sars-cov-2 variants, spike mutations and immune escape.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Sars-cov-2 variants, spike mutations and immune escape

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:23.489130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 174e992e-e29b-4c9a-b45d-036d299f5507 · outbound

This paper cites Reduced sensitivity of sars-cov-2 variant delta to antibody neutralization.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Reduced sensitivity of sars-cov-2 variant delta to antibody neutralization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:23.315160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c43cf14-acd6-4d3b-a6b6-9573677664c2 · outbound

This paper cites Evidence of escape of sars-cov-2 variant b.1.351 from natural and vaccine-induced sera.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Evidence of escape of sars-cov-2 variant b.1.351 from natural and vaccine-induced sera

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:23.140001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 484163d6-ec49-49a6-9ce3-598771703ceb · outbound

This paper cites Research on expansion and classification of imbalanced data based on smote algorithm.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Research on expansion and classification of imbalanced data based on smote algorithm

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:22.932772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51951029-55da-4eba-93fd-36e200fcfa30 · outbound

This paper cites Challenges and limitations of synthetic minority oversampling techniques in machine learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Challenges and limitations of synthetic minority oversampling techniques in machine learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:22.771077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cac9dab-ad82-4e3f-bed1-0df8a6a847fe · outbound

This paper cites Unified rational protein engineering with sequence-based deep representation learning.

CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data Unified rational protein engineering with sequence-based deep representation learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:22.602468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

Pith citing papers