MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins

Guan, Shixuan; Qian, Yuqing; Jiang, Tengsheng; Ding, Yongsheng; Jiang, M.; Wu, Haisi

doi:10.1109/tcbb.2022.3183191

Cited by 2 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [18], the understanding of protein-DNA interactions depended on identifying DNA-binding proteins (DBPs). Traditional experimental techniques for identifying DBPs required a significant amount of time and effort.…”

Section: Prior Workmentioning

confidence: 99%

DNA recognition using Novel Deep Learning Model

Al-Kaltakchi,

Abdulla,

Al-Nima

2024

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.

show abstract

Section: Prior Workmentioning

confidence: 99%

DNA recognition using Novel Deep Learning Model

Al-Kaltakchi,

Abdulla,

Al-Nima

2024

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

show abstract

Identification of DNA-binding protein based multiple kernel model

Qian

Shang

Guo

et al. 2023

MBE

View full text Add to dashboard Cite

<abstract> <p>DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://8.130.69.121:8082/">http://8.130.69.121:8082/</ext-link>.</p> </abstract>

show abstract

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins

Cited by 2 publications

References 44 publications

DNA recognition using Novel Deep Learning Model

DNA recognition using Novel Deep Learning Model

Identification of DNA-binding protein based multiple kernel model

Contact Info

Product

Resources

About