Applications and challenges of high performance computing in genomics

Jiang, Meiye; Bu, Congfan; Zeng, Jingyao; Du, Zhenglin; Xiao, Jingfa

doi:10.1007/s42514-021-00081-w

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…Combining high-performance computing and grid computing models is a practice in many scientific workflows to increase the computational volume of high-performance computing by combining distributed grid resources. is method has been widely used in many projects [9,10]. For computing tasks with computing performance as the bottleneck, this paper proposes a scientific cloud such as grid cloud.…”

Section: Approaches To High-performance Computingmentioning

confidence: 99%

English Language Features in Linguistics by High-Performance Computing

Chen

Awang

Kadir

2022

Mobile Information Systems

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High-performance computing clusters are mainly used to deal with complex computing problems and are widely used in the fields of meteorology, ocean, environment, life science, and computer-aided engineering. Language is the way humans communicate and communicate. Linguistic features are the stylistic features that distinguish all languages from other languages. This paper aims to study how to analyze English language features based on high-performance computing. This paper addresses the problem of linguistic feature analysis, which is built on high-performance computing. Therefore, this paper expounds the related concepts and algorithms, and designs and analyzes the characteristics of English language. The experimental results show that among the 160 English sentences in two different journals, complex sentences are the most used, with a total of 55 sentences, accounting for 34.38%. The second is mixed sentence types, 47 of which are mixed sentence structures, accounting for 29.38%. Among them, the combination of simple sentences + coordinating complex sentences + complex sentences constitutes the most mixed sentences, which appear 12 times and 8 times in ELT Journal and SSCI, respectively, accounting for 15.00% and 10.00% of their respective corpora.

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Section: Approaches To High-performance Computingmentioning

confidence: 99%

English Language Features in Linguistics by High-Performance Computing

Chen

Awang

Kadir

2022

Mobile Information Systems

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“… 10 Given the vast volume of data, genomics stores and analyzes data using bioinformatics and deep learning methods. 11 It is in this context that our work is situated.…”

Section: Introductionmentioning

confidence: 99%

Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model

Ben Nasr Barber,

Elloumi Oueslati

2024

Journal of Genetic Engineering and Biotechnology

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“…Alternatively, this challenge can be addressed by making use of the currently available experimental PNAI profiles and quantitative binding affinity data on wild-type and mutant PNA complexes. With this comprehensive data in hand, there is a pressing need to convert it into meaningful information using recent developments in artificial intelligence and ML. , The ability of the ML model to “learn” intrinsic patterns in a complex plane of available data has resulted in resource-optimal predictions that do not compromise accuracy. Employing these technological advancements, PNAI strength can be quantified rapidly from their sequence, especially when the existing experimental procedures fall short.…”

Section: Introductionmentioning

confidence: 99%

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

Pandey,

Behara,

Sharma

et al. 2024

J. Chem. Inf. Model.

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Predicting the protein–nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting K D and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface:

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Applications and challenges of high performance computing in genomics

Cited by 8 publications

References 43 publications

English Language Features in Linguistics by High-Performance Computing

English Language Features in Linguistics by High-Performance Computing

Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

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