Pattern analysis of genetics and genomics: a survey of the state-of-art

Chaki, Jyotismita; Dey, Nilanjan

doi:10.1007/s11042-019-7181-8

Cited by 11 publications

(5 citation statements)

References 226 publications

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“…In contrast to conventional ML algorithms that depend on manual feature selection methods, DNN-based methods have an inbuilt mechanism for feature extraction [24]. DNN methods also have a better classification performance [25] as compared to traditional ML algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

et al. 2021

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Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

et al. 2021

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“…Rank transformation is one of the normalisation methods that can greatly influence the outcomes of gene expression analysis [49]. The method is used to remove technological noise that characterises genomic data [50].…”

Section: ) Implementation Of Rank Transformationmentioning

confidence: 99%

Stacking and Chaining of Normalization Methods in Deep Learning-Based Classification of Colorectal Cancer Using Gut Microbiome Data

et al. 2021

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Machine learning (ML)-based detection of diseases using sequence based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non-invasive alternative in colorectal cancer detection, where data can be obtained from stool samples. Considering limitations of existing methods for CRC detection, medical research has shown interest in the use of high throughput data to identify the disease. Owing to several limitations of conventional ML algorithms, deep learning (DL) methods are becoming more popular due to their outstanding performance in related fields. However, the performance of DL methods is affected by limitations such as dimensionality, sparsity, and feature dominance inherent in microbiome data. This research proposes stacking and chaining of normalisation methods to address the limitations. While the stacking technique offers a robust, easy to use, and interpretable alternative for augmenting microbiome and other tabular data, the chaining technique is an alternative to data normalisation that dynamically adjusts the underlying properties of data towards the normal distribution. The proposed techniques are combined with rank transformation and feature selection to further improve the performance of the model, with area under the curve (AUC) values between 0.857 to 0.987using publicly available datasets.

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“…The pattern analysis field provides efficient computer-based techniques that enable humans, particularly bioinformaticians, to analyze complex and large genetic and genomic data [ 7 ]. SPM [ 8 ], a special case of structured data mining, has been applied in genomics to find patterns of specific elements in genes [ 9 ], to analyze gene expression [ 10 ], to mine maximal contiguous frequent patterns from DNA sequence datasets [ 11 ], to discover motifs in DNA sequences [ 12 ], to predict protein function [ 13 ] and diseases [ 14 ], to discover gene interactions and their characterizations [ 15 ], to interpret patterns extracted from DNA microarrays [ 16 ], to mine k-mers [ 17 ] and to construct the phylogenetic tree [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Using artificial intelligence techniques for COVID-19 genome analysis

et al. 2021

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The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

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Pattern analysis of genetics and genomics: a survey of the state-of-art

Cited by 11 publications

References 226 publications

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Stacking and Chaining of Normalization Methods in Deep Learning-Based Classification of Colorectal Cancer Using Gut Microbiome Data

Using artificial intelligence techniques for COVID-19 genome analysis

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