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Genome pairwise sequence alignment is one of the most computationally intensive workloads in many genomic pipelines, often accounting for over 90% of the runtime of critical bioinformatics applications. Recent advancements in sequencing technologies keep ...

Metagenomics is a rapidly expanding field that uses next-generation sequencing technology to analyze the genetic makeup of environmental samples. However, accurately identifying the organisms in a metagenomic sample can be complex, and ...

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• The study evaluated a large set of compressors for taxonomic identification in metagenomics.
• The Normalized Compression (NC) measure was used to evaluate the complexity of the data and the effectiveness of the compressors.
• It found ...

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting ...

• GenArchBench is the first genomics benchmark suite targeting the Arm architecture.
• GenArchBench introduces SVE implementations of kernels that cannot be auto-vectorized.
• Arm SVE enhances code maintainability and binary portability ...

The analysis and interpretation of high-dimensional biological data sets is a challenging task. Exploratory data analysis of count data produced by next-generation sequencing technologies presents a common hurdle to researchers. Biologists often find it ...

In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes. Metformin is the primary treatment for T2DM, but 35% of patients ...

• A detailed version of the methods section is present in the supplementary information.
• Age gap (8 years) between control and treatment groups may impact the ML model; future research should address this.
• This work innovates with ...

Infertility affects ∼15% of couples globally and half of cases are related to genetic disorders. Despite growing data and unprecedented improvements in high-throughput sequencing technologies, accumulated fertility-related issues concerning ...

• Built the most comprehensive resource integrating infertility associated genetic, clinical factors and modulators.
• Design a series of customized research applications for researchers and clinicians to decipher in-depth disease ...

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerg- ing methods rely heavily on large scale computational resources ...

Biological big data are a massive amount of data generated from multi-omics experiments, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, glycomics, epigenomics, and other omics. These data are used to study biological ...

Determining the role of a DNA variant in patients’ health status – a process known as variant interpretation – is highly critical for precision medicine applications. Variant interpretation involves a complex process where, regrettably, there is ...

As genomic research becomes more complex and data-rich, artificial intelligence (AI) has emerged as a crucial tool for processing and analyzing high-dimensional genomic data, accelerating biomarker discovery, and enhancing genomic sequence ...

The recent progress in phenotypic information and machine learning has led to a remarkable development in the accuracy of binary seizure detection. Yet the performance of classifying specific seizure types remains suboptimal due to the limited ...

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse ...

• Recent progress in the use and integration of “multi-omics” techniques to identify novel marine metabolites.
• The multi-omics data integration tools developed for analyzing “multi-omics” data.
• The requirement of ML for analyzing “...

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously ...

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these ...

The ever-increasing yields in genome sequence data production pose a computational challenge to current genome sequence analysis tools, jeopardizing the future of personalized medicine. Leveraging hardware accelerators (GPUs, FPGAs, and ASICs) to ...

FASTQ format is a text-based format for storing a biological sequence (usually nucleotide sequence) and its corresponding quality scores used for genome sequencing. While most text-based formats compress well using traditional methods such as tar and ...

The identification of genetic variations in large cohorts is a critical issue to identify patient cohorts, disease risks, and to develop more effective treatments. To help this analysis, we improved a variant calling pipeline for the human genome ...

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• ML can enhance diagnostic accuracy in rare genetic diseases.
• We identified key features for ML models in sequencing data.
• This systematic review may support ML model development in rare disease diagnosis.

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to ...

Influenza viruses pose great threats to public health and cause enormous economic losses every year. Previous work has revealed the viral factors associated with the virulence of influenza viruses in mammals. However, taking prior viral knowledge ...

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The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find ...