Graphical classification of DNA sequences of HLA alleles by deep learning

Miyake, Jun; Kaneshita, Yuhei; Asatani, Satoshi; Tagawa, Seiichi; Niioka, Hirohiko; Hirano, Takashi

doi:10.1007/s13577-017-0194-6

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…Deep Learning approaches have also revolutionized the field of cancer vaccinology through the improved prediction of neoantigens and their HLA binding affinity (Sher et al, 2017 ; Tran et al, 2019 ; Wu et al, 2019 ). Autoencoders of deep learning have shown promising improvement in extracting characteristics of human Leukocyte Antigen (HLA-A), which could be utilized in both transplantations and vaccine discovery (Miyake et al, 2018 ).…”

Section: Background Of Machine Learning Methods For Therapy Discoverymentioning

confidence: 99%

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Arshadi

Webb

Salem

et al. 2020

Front. Artif. Intell.

168

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SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

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Section: Background Of Machine Learning Methods For Therapy Discoverymentioning

confidence: 99%

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Arshadi

Webb

Salem

et al. 2020

Front. Artif. Intell.

168

View full text Add to dashboard Cite

show abstract

“…Genetic analysis by autoencoder that we have already reported (Miyake et al 2018) was used in this study. Namely, we applied the document vector method (the nucleotide sequence was replaced by a vector (4 5 = 1,024 dimensions) with a normalized histogram of 1,024 words consisting of 5-mer tiny nucleotide sequences without alignment).…”

Section: Methodsmentioning

confidence: 99%

“…We have been studying the application of deep-learning autoencoder for analyzing gene sequences (Miyake et al 2018). The feature extraction capability of autoencoder is useful for this kind of analysis.…”

Section: Introductionmentioning

confidence: 99%

“…There is no need to organize the potentially characteristic sites in the gene beforehand. In our previous study of the human leukocyte antigen A (HLA-A) gene, we discovered that autoencoder can correctly represent and classify differences in HLA-A alleles (Miyake et al 2018). Autoencoder has the potential to extract the genetic characteristics of a gene at a level close to human recognition.…”

Section: Introductionmentioning

confidence: 99%

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Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions

Miyake

Sato

Baba

et al. 2021

Preprint

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We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E.

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“…The copyright holder for this preprint this version posted June 22, 2021. ; https://doi.org/10.1101/2021.06.22.449384 doi: bioRxiv preprint genomic sequences. In this study, the value of was heuristically determined as (Miyake et al 2018), resulting in the vector size of 1024. We applied this method to all of the 360 human mitochondrial sequences and created a ×1024 matrix by combining the vectors by rows.…”

Section: Methodsmentioning

confidence: 99%

Extracting Phylogenetic Information of Human Mitochondrial DNA by Linear Autoencoder

Shimazaki

Nishimoto

Niioka

et al. 2021

Preprint

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We used a linear autoencoder (LAE) and its learning dynamics to analyze the high-order structure of human mitochondrial DNA (mtDNA). A total of 360 complete human mtDNA sequences were collected from the MITOMAP database and transformed into 1024-dimensional vectors of pentanucleotide frequencies. We compressed those into a three-dimensional (3D) coordinates by an LAE at each step of training by gradient descent with respect to the quadratic error function. Along the time axis of training epochs, the compressed 3D coordinates were gradually clustered and separated in accordance with the order of the genetic distance in the phylogenetic tree of human mtDNA haplogroups. This suggests that there is an association between the learning dynamics of LAE and the high-dimensional structure of human mtDNA sequences, similar to that of phylogenetic analysis and evolutionary pathways: the five clusters eventually contained only a single haplogroup of L0, M, N, R, and U, while the L3 cluster contained a small number of M members and The packing was comparable to that realized in learning dynamics similar to genetic classification and evolutionary pathways by LAE in principal component analysis (PCA), but somewhat denser than PCA.

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Graphical classification of DNA sequences of HLA alleles by deep learning

Cited by 10 publications

References 4 publications

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions

Extracting Phylogenetic Information of Human Mitochondrial DNA by Linear Autoencoder

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