article · Measurement Sensors
Matching genome sequences is critical for health analytics, treatment planning, and rapidly identifying disease outbreaks in healthcare 4.0. To handle large-scale data via connected smart devices, an Internet of Things-enabled hybrid model for patient genome sequence analysis has been developed. The approach integrates Hadoop concepts, OpenCL, and an application programming interface to improve pattern matching. At its core, the system accelerates the standard Basic Local Alignment Search Tool (BLAST) algorithm used for biological sequence comparisons. By distributing sequence strings into blocks across mapper and reducer processes, the architecture incorporates both inter-node and intra-node fine-grained parallelism. This framework optimises the use of shared computing resources and substantially speeds up processing times. When tested across multiple datasets, the accelerated model consistently achieved superior sequence analysis performance compared to traditional algorithmic alternatives.
Fast and accurate genome sequence matching is essential for detecting disease outbreaks and tailoring treatments. As genomic datasets expand, existing computational tools struggle with the processing burden. Enhancing alignment algorithms using smart, interconnected computing architectures allows healthcare systems to analyse complex biological data much faster, supporting timely clinical decisions and more effective public health monitoring.
The model could assist bioinformatics software developers, diagnostic laboratories, and digital health providers seeking faster genomic analysis on distributed computing hardware. Because the abstract demonstrates algorithmic testing across datasets rather than clinical deployment or field testing on physical devices, the technology appears to be applied research at an experimental stage, requiring integration testing in healthcare infrastructure before commercial use.
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Genome sequence matching is vital for health analytics and treatment in healthcare 4.0. It focuses on finding whether a given sequence resembles other sequences that can help identify disease outbreaks faster. The healthcare 4.0 primarily require more efficient systems that can easily connect and interact with big data. The Internet of things (IoT) can potentially realize smart devices that can perform this role in the health sector. This research presents an IoT-enabled hybrid model for Genome sequence analysis of patients in healthcare 4.0. The proposed model utilizes an improved pattern-matching technique that uses Hadoop's ideas, OpenCL, and APAR (API). The primary local alignment search tool (BLAST) is the core algorithm extensively used to compare and find preliminary biological sequence information and calculate the matches' statistical importance. BLAST enables many strings to be distributed in the form of blocks; after that, an instance of the Mapper is mapped to process it after the output of all the mappers combined through the reducer. This mapper and reducer process has inter-node parallelism to speed up the process and efficiently utilize the shared resources. In this work, fine-grained parallelism has been introduced in Hadoop-based BLAST. The proposed work also provided inter-node and intra-node parallelism. The Mapper and reducer accelerated the conventional BLAST algorithm. The proposed model achieved better results than other traditional algorithms for different datasets in all cases.
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DOI: 10.1016/j.measen.2023.100679
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