Multifactorial feature extraction and site prognosis model for protein methylation data

Khandelwal, Monika; Rout, Ranjeet Kumar; Umer, Saiyed; Mallik, Saurav; Li, Aimin

doi:10.1093/bfgp/elac034

Cited by 10 publications

(5 citation statements)

References 49 publications

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“…However, CADD has its own challenges [126][127][128][129][130]. We summarize below the challenges in respect of CADD, from traditional methods to molecule generation using deep learning.…”

Section: Discussionmentioning

confidence: 99%

Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

Choudhuri

Yendluri

Poddar

et al. 2023

Kinases and Phosphatases

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The goal of drug discovery is to uncover new molecules with specific chemical properties that can be used to cure diseases. With the accessibility of machine learning techniques, the approach used in this search has become a significant component in computer science in recent years. To meet the Precision Medicine Initiative’s goals and the additional obstacles that they have created, it is vital to develop strong, consistent, and repeatable computational approaches. Predictive models based on machine learning are becoming increasingly crucial in preclinical investigations. In discovering novel pharmaceuticals, this step substantially reduces expenses and research times. The human kinome contains various kinase enzymes that play vital roles through catalyzing protein phosphorylation. Interestingly, the dysregulation of kinases causes various human diseases, viz., cancer, cardiovascular disease, and several neuro-degenerative disorders. Thus, inhibitors of specific kinases can treat those diseases through blocking their activity as well as restoring normal cellular signaling. This review article discusses recent advancements in computational drug design algorithms through machine learning and deep learning and the computational drug design of kinase enzymes. Analyzing the current state-of-the-art in this sector will offer us a sense of where cheminformatics may evolve in the near future and the limitations and beneficial outcomes it has produced. The approaches utilized to model molecular data, the biological problems addressed, and the machine learning algorithms employed for drug discovery in recent years will be the emphasis of this review.

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“…However, CADD has its own challenges [126][127][128][129][130]. We summarize below the challenges in respect of CADD, from traditional methods to molecule generation using deep learning.…”

Section: Discussionmentioning

confidence: 99%

Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

Choudhuri

Yendluri

Poddar

et al. 2023

Kinases and Phosphatases

Self Cite

View full text Add to dashboard Cite

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“…where p i indicates the probability of two values regarding the binary sequence, and SE is used to compute the uncertainty in a binary string (Khandelwal et al, 2022a). When the probability p = 0, the event is assured never to happen, resulting in no uncertainty and entropy of 0.…”

Section: Shannon Entropy and Modified Shannon Entropymentioning

confidence: 99%

“…The SE uses the probability p of the two possibilities (0/1) to calculate information entropy. The following equation gives the SE of a binary sequence:

where p i indicates the probability of two values regarding the binary sequence, and SE is used to compute the uncertainty in a binary string ( Khandelwal et al, 2022a ). When the probability p = 0, the event is assured never to happen, resulting in no uncertainty and entropy of 0.…”

Section: Basic Terminologymentioning

confidence: 99%

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

et al. 2023

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Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology.

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“…Feature engineering approaches involve extracting informative features from sequences and using them for classification [37,22,2]. These features include amino acid composition, dipeptide composition, and physicochemical properties.…”

Section: Related Workmentioning

confidence: 99%

Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification

Ali,

Mansoor,

Chourasia

et al. 2023

Preprint

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Biological sequence classification is vital in various fields, such as genomics and bioinformatics. The advancement and reduced cost of genomic sequencing have brought the attention of researchers for protein and nucleotide sequence classification. Traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences, while numerous machine-learning models have been proposed to tackle this challenge. In this work, we propose Hist2Vec, a novel kernel-based embedding generation approach for capturing sequence similarities. Hist2Vec combines the concept of histogram-based kernel matrices and Gaussian kernel functions. It constructs histogram-based representations using the uniquek-mers present in the sequences. By leveraging the power of Gaussian kernels, Hist2Vec transforms these representations into high-dimensional feature spaces, preserving important sequence information. Hist2Vec aims to address the limitations of existing methods by capturing sequence similarities in a high-dimensional feature space while providing a robust and efficient framework for classification. We employ kernel Principal Component Analysis (PCA) using standard machine-learning algorithms to generate embedding for efficient classification. Experimental evaluations on protein and nucleotide datasets demonstrate the efficacy of Hist2Vec in achieving high classification accuracy compared to state-of-the-art methods. It outperforms state-of-the-art methods by achieving >76% and >83% accuracies for DNA and Protein datasets, respectively. Hist2Vec provides a robust framework for biological sequence classification, enabling better classification and promising avenues for further analysis of biological data.

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Multifactorial feature extraction and site prognosis model for protein methylation data

Cited by 10 publications

References 49 publications

Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification

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