Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN

Tharmakulasingam, Mukunthan; Topal, Cihan; Fernando, Anil; Ragione, Roberto La

doi:10.1145/3375923.3375956

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Filters, wrappers, and embedded methods are three types of feature selection processes [41]; filter methods measure the intrinsic properties of features based on univariate statistics metrics such as variance, mutual information, Chi-square(χ 2 ), information gain, correlation, etc. Wrapper methods, such as sequential feature selection and heuristic search algorithms, select a subset of features by applying an evaluation function that is optimised using a machine learning technique: forward selection, backward elimination and recursive feature elimination are a few examples of sequential feature selection methods [42]- [44]. Wrapper methods select the features by measuring their contribution to classifier performance in an iterative manner; therefore, wrapper methods are more computationally complex as compared to filter methods since they require repeated steps and cross-validation.…”

Section: Dimension Reduction and Model Selectionmentioning

confidence: 99%

Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing Labels

Tharmakulasingam

Brian

Ragione

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Predicting Antimicrobial Resistance (AMR) from genomic data has important implications for human and animal healthcare, and especially given its potential for more rapid diagnostics and informed treatment choices. With the recent advances in sequencing technologies, applying machine learning techniques for AMR prediction have indicated promising results. Despite this, there are shortcomings in the literature concerning methodologies suitable for multi-drug AMR prediction and especially where samples with missing labels exist. To address this shortcoming, we introduce a Rectified Classifier Chain (RCC) method for predicting multi-drug resistance. This RCC method was tested using annotated features of genomics sequences and compared with similar multi-label classification methodologies. We found that applying the eXtreme Gradient Boosting (XGBoost) base model to our RCC model outperformed the second-best model, XGBoost based binary relevance model, by 3.3% in Hamming accuracy and 7.8% in F1-score. Additionally, we note that in the literature machine learning models applied to AMR prediction typically are unsuitable for identifying biomarkers informative of their decisions; in this study, we show that biomarkers contributing to AMR prediction can also be identified using the proposed RCC method. We expect this can facilitate genome annotation and pave the path towards identifying new biomarkers indicative of AMR.

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Section: Dimension Reduction and Model Selectionmentioning

confidence: 99%

Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing Labels

Tharmakulasingam

Brian

Ragione

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Identifying biomarkers from genomic sequences contributing to the predictions and applying dimensional reduction techniques for this data is crucial for achieving higher accuracy in predicting AMR from genome sequence data [7]. Although certain machine learning models give distinct feature sets that domain experts can further interpret [28], [29], deep learning models act as black-box models whose results cannot be easily interpreted. Existing deep learning models are deficient in returning the feature set and weights contributing to the classification decision, thereby making hard to interpret the model and results.…”

Section: Explainable Models For Amr Predictionmentioning

confidence: 99%

Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels

et al. 2022

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Predicting Antimicrobial Resistance (AMR) from genomic sequence data has become a significant component of overcoming the AMR challenge, especially given its potential for facilitating more rapid diagnostics and personalised antibiotic treatments. With the recent advances in sequencing technologies and computing power, deep learning models for genomic sequence data have been widely adopted to predict AMR more reliably and error-free. There are many different types of AMR; therefore, any practical AMR prediction system must be able to identify multiple AMRs present in a genomic sequence. Unfortunately, most genomic sequence datasets do not have all the labels marked, thereby making a deep learning modelling approach challenging owing to its reliance on labels for reliability and accuracy. This paper addresses this issue by presenting an effective deep learning solution, Mask-Loss 1D convolution neural network (ML-ConvNet), for AMR prediction on datasets with many missing labels. The core component of ML-ConvNet utilises a masked loss function that overcomes the effect of missing labels in predicting AMR. The proposed ML-ConvNet is demonstrated to outperform state-of-the-art methods in the literature by 10.5%, according to the F1 score. The proposed model's performance is evaluated using different degrees of the missing label and is found to outperform the conventional approach by 76% in the F1 score when 86.68% of labels are missing. Furthermore, the ML-ConvNet was established with an explainable artificial intelligence (XAI) pipeline, thereby making it ideally suited for hospital and healthcare settings, where model interpretability is an essential requirement.INDEX TERMS Multilabel classification, deep neural network, multi-drug AMR, missing labels, explainable AI.

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“…Santos et al studied the influence of different distance functions on KNN in imputation of missing data [35]. Tharmakulasingam et al used non-Euclidean distance and KNN to improve pathogen identification [36]. Wang et al proposed a new KNN algorithm through similarity and location distance [37].…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

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Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN

Cited by 4 publications

References 11 publications

Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing Labels

Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing Labels

Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels

KNN Classification with One-step Computation

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