A non-invasive cancer gene detection technique using FLANN based adaptive filter

Roy, Saikat Singha; Barman, Soma

doi:10.1007/s00542-018-4036-6

Cited by 10 publications

(2 citation statements)

References 88 publications

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“…This approach has the advantage that the time required to test new samples is relatively faster than the alignment approach, because the new samples to be tested are entered into the model without being matched one by one to the relevant reference samples. Several studies have used the machine learning approach, namely SNVHMM [ 13 ], Bayesian based Somatic Structural Variation [ 14 ], Functional Link Artificial Neural Network [ 15 ], Tumor Covariate Signature Model [ 16 ], DriverML [ 17 ], Multi-Modal Deep Boltzmann Machine [ 18 ], NeoSomatic [ 19 ], circulating tumor DNA detection [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

et al. 2023

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Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient’s DNA sequence, without the need for reference sequences or other additional tools.

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Section: Introductionmentioning

confidence: 99%

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

et al. 2023

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“…Filter is a kind of noise elimination method which is widely used in signal processing. The filter effectively filters out specific frequency points in the signal, after which signals of specific frequency can be obtained [3]. There are many researches on the specific functions of filters in the aspects of network noise elimination and signal enhancement.…”

Section: Introductionmentioning

confidence: 99%

Signal enhancement in wireless sensor networks based on adaptive filters

Tanida¹

2023

J. meas. eng.

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Wireless sensor networks are widely used in communication, medical treatment, radar and detection. With the vigorous development of computer science and intelligent technology, wireless sensor networks are also constantly improving in the development. Sensor networks are prone to noise interference when input signals, which will affect the estimation accuracy of the network. In order to enhance the signal of sensor network and improve its accuracy, a distributed filtering algorithm based on fusion adaptive weighting is proposed. Before building the model, the experiment first studied the three traditional adaptive filtering algorithms, LMS, RLS and AP, as the basis for building the experimental model. Then, combined with the distributed characteristics of the sensor network, the attributes of the nodes and their influence in the network were considered in the experiment, and the importance and support of the nodes were linearly weighted to obtain the estimated certainty of each sensor node to the target. Finally, a fusion adaptive weighted distributed filtering algorithm is constructed in the experiment. The simulation experiment verifies that the constructed model can reduce the noise interference to a certain extent, which is conducive to the enhancement of its network signal, and its error estimation accuracy is also improved.

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A genomic signal processing approach for identification and classification of coronavirus sequences

Khodaei,

Mozaffari-Tazehkand,

Sharifi

2024

Soft Comput

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A non-invasive cancer gene detection technique using FLANN based adaptive filter

Cited by 10 publications

References 88 publications

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

Signal enhancement in wireless sensor networks based on adaptive filters

A genomic signal processing approach for identification and classification of coronavirus sequences

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