Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Shrimankar, Deepti D.; Durge, Aditi; Sawarkar, Ankush D.

doi:10.2174/1389202923666220927105311

Cited by 3 publications

(2 citation statements)

References 76 publications

(127 reference statements)

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“…This could involve collaborating with biologists to apply our methods to actual biological datasets, such as those derived from clinical samples or environmental studies. Additionally, exploring the integration of our algorithms with existing bioinformatics tools and pipelines could enhance their usability and impact [26].…”

Section: Discussionmentioning

confidence: 99%

A Novel Fuzzy Bi-Clustering Algorithm with Axiomatic Fuzzy Set for Identification of Co-Regulated Genes

Xu,

Wang

2024

Mathematics

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The identification of co-regulated genes and their Transcription-Factor Binding Sites (TFBSs) are the key steps toward understanding transcription regulation. In addition to effective laboratory assays, various bi-clustering algorithms for the detection of the co-expressed genes have been developed. Bi-clustering methods are used to discover subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. By building two fuzzy partition matrices of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this paper proposes a novel fuzzy bi-clustering algorithm for the identification of co-regulated genes. Specifically, the gene expression data are transformed into two fuzzy partition matrices via the sub-preference relations theory of AFS at first. One of the matrices considers the genes as the universe and the conditions as the concept, and the other one considers the genes as the concept and the conditions as the universe. The identification of the co-regulated genes (bi-clusters) is carried out on the two partition matrices at the same time. Then, a novel fuzzy-based similarity criterion is defined based on the partition matrices, and a cyclic optimization algorithm is designed to discover the significant bi-clusters at the expression level. The above procedures guarantee that the generated bi-clusters have more significant expression values than those extracted by the traditional bi-clustering methods. Finally, the performance of the proposed method is evaluated with the performance of the three well-known bi-clustering algorithms on publicly available real microarray datasets. The experimental results are in agreement with the theoretical analysis and show that the proposed algorithm can effectively detect the co-regulated genes without any prior knowledge of the gene expression data.

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Section: Discussionmentioning

confidence: 99%

A Novel Fuzzy Bi-Clustering Algorithm with Axiomatic Fuzzy Set for Identification of Co-Regulated Genes

Xu,

Wang

2024

Mathematics

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“…Genomic data sequences are capable of representing a wide variety of information about the underlying species. For instance, in human beings, the genomic sequences assist in the presence of cancer, diabetes, heart and stroke issues, and other diseases [ 48 ]. Genetic specialists claim that the SARS-CoV-2 virus, which was first detected in Wuhan, China, has already infected millions of people throughout the world [ 49 ].…”

Section: Detailed Review Of Existing Genome Processing Modelsmentioning

confidence: 99%

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review

Ahuja,

Shrimankar,

Durge

2023

Self Cite

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Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incrementalperformance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

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DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences

Durge,

Shrimankar

2024

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Problem: Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets. Aim: This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection- based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. Methods: The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Results: Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. Conclusion: DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

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Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Cited by 3 publications

References 76 publications

A Novel Fuzzy Bi-Clustering Algorithm with Axiomatic Fuzzy Set for Identification of Co-Regulated Genes

A Novel Fuzzy Bi-Clustering Algorithm with Axiomatic Fuzzy Set for Identification of Co-Regulated Genes

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences

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