-
Comparative Analysis of Deep Generative Model for Industrial Enzyme Design Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-16 Beibei Zhang, Qiaozhen Meng, Chengwei Ai, Guihua Duan, Ercheng Wang, Fei Guo
: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions
-
Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-15 Dongqing Su, Honghao Li, Tao Wang, Min Zou, Haodong Wei, Yuqiang Xiong, Hongmei Sun, Shiyuan Wang, Qilemuge Xi, Yongchun Zuo, Lei Yang
Background: Mutations in metabolism-related genes in somatic cells potentially lead to disruption of metabolic pathways, which results in patients exhibiting different molecular and pathological features. background: Mutations in metabolism-related genes in somatic cells potentially lead to disruption of metabolic pathways, which results in patients exhibiting different molecular and pathological features
-
An Effective Method to Identify Cooperation Driver Gene Sets Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-15 Wei Zhang, Yifu Zeng, Bihai Zhao, Jie Xiong, Tuanfei Zhu, Jingjing Wang, Guiji Li, Lei Wang
Background: In cancer genomics research, identifying driver genes is a challenging task. Detecting cancer-driver genes can further our understanding of cancer risk factors and promote the development of personalized treatments. Gene mutations show mutual exclusivity and cooccur, and most of the existing methods focus on identifying driver pathways or driver gene sets through the study of mutual exclusivity
-
GB5mCPred: Cross-species 5mc Site Predictor Based on Bootstrap-based Stochastic Gradient Boosting Method for Poaceae Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-15 Dipro Sinha, Tanwy Dasmandal, Md Yeasin, D.C Mishra, Anil Rai, Sunil Archak
Background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money-intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. background: One of the most
-
DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-03 Zixu Wang, Yangyang Chen, Xiulan Guo, Yayang Li, Pengyong Li, Chunyan Li, Xiucai Ye, Tetsuya Sakurai
Background: The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However, an emerging paradigm in the generation domain is diffusion models, which treat data non-autoregressively
-
Research on the Mechanism of Traditional Chinese Medicine Treatment for Diseases caused by Human Coronavirus COVID-19 Curr. Bioinform. (IF 4.0) Pub Date : 2024-04-02 Xian-Fang Wang, Chong-Yang Ma, Zhi-Yong Du, Yi-Feng Liu, Shao-Hui Ma, Sang Yu, Rui-xia Jin, Dong-qing Wei
Background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability, it poses a great threat to global human health and is a major problem currently faced by the medical and health fields. background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication
-
CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning Curr. Bioinform. (IF 4.0) Pub Date : 2024-03-29 Bing Rao, Bing Han, Leyi Wei, Zeyu Zhang, Xinbo Jiang, Balachandran Manavalan
Background:: With the increasing development of biotechnology, many cancer solutions have been proposed nowadays. In recent years, Neo-peptides-based methods have made significant contributions, with an essential prerequisite of bindings between peptides and HLA molecules. However, the binding is hard to predict, and the accuracy is expected to improve further. Methods:: Therefore, we propose the Crossed
-
Relational Graph Convolution Network with Multi Features for AntiCOVID-19 Drugs Discovery using 3CLpro Potential Target Curr. Bioinform. (IF 4.0) Pub Date : 2024-03-11 Medard Edmund Mswahili, Goodwill Erasmo Ndomba, Young Jin Kim, Kyuri Jo, Young-Seob Jeong
Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the
-
An Extended Feature Representation Technique for Predicting Sequenced-based Host-pathogen Protein-protein Interaction Curr. Bioinform. (IF 4.0) Pub Date : 2024-03-11 Jerry Emmanuel, Itunuoluwa Isewon, Grace Olasehinde, Jelili Oyelade
Background: The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature, two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed, studies
-
A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression Curr. Bioinform. (IF 4.0) Pub Date : 2024-03-11 Karthikeyan A. Vijayakumar, Gwang-Won Cho
Introduction: The recent advancement in artificial intelligence has compelled medical research to adapt the technologies. The abundance of molecular data and AI technology has helped in explaining various diseases, even cancers. Schizophrenia is a complex neuropsychological disease whose etiology is unknown. Several gene-wide association studies attempted to narrow down the cause of the disease but
-
MCHAN: Prediction of Human Microbe-drug Associations Based on Multiview Contrastive Hypergraph Attention Network Curr. Bioinform. (IF 4.0) Pub Date : 2024-03-01 Guanghui Li, Ziyan Cao, Cheng Liang, Qiu Xiao, Jiawei Luo
Background: Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence, the advancement
-
Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-22 Efendi Zaenudin, Ezra B. Wijaya, Venugopala Reddy Mekala, Ka-Lok Ng
Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have
-
A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-20 Chuqi Lei, Senbiao Fang, Yaohang Li, Fei Guo, Min Li
background: computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods treat the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher
-
Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-09 Jia Zheng, Yetong Zhou
Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression
-
Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-07 Yuanyuan He, Haodong Wei, Siqing Liao, Ruiming Ou, Yuqiang Xiong, Yongchun Zuo, Lei Yang
Background: Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer. Purpose: This study aims to establish a prognostic prediction model for
-
Genotype and Phenotype Association Analysis Based on Multi-omics Statistical Data Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-07 Xinpeng Guo, Yafei Song, Dongyan Xu, Xueping Jin, Xuequn Shang
Background: When using clinical data for multi-omics analysis, there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy, the requirements of data management by various institutions, and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships
-
Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-07 Runhua Zhang, Baozhong Zhu, Tengsheng Jiang, Zhiming Cui, Hongjie Wu
Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on
-
Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-04 Weixin Xie, Jiayu Xu, Chengkui Zhao, Jin Li, Shuangze Han, Tianyu Shao, Limei Wang, Weixing Feng
Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models
-
Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-04 Dayu Tan, Jing Wang, Zhaolong Cheng, Yansen Su, Chunhou Zheng
Objective: This work aims to infer causal relationships between genes and construct dynamic gene regulatory networks using time-course scRNA-seq data. Methods: We propose an analytical method for inferring GRNs from single-cell time-course data based on temporal convolutional networks (scTGRN), which provides a supervised learning approach to infer causal relationships among genes. scTGRN constructs
-
STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-02 Liu Fan, Xiaoyu Yang, Lei Wang, Xianyou Zhu
Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships
-
DeepPTM: Protein Post-translational Modification Prediction from Protein Sequences by Combining Deep Protein Language Model with Vision Transformers Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-02 Necla Nisa Soylu, Emre Sefer
Introduction:: More recent self-supervised deep language models, such as Bidirectional Encoder Representations from Transformers (BERT), have performed the best on some language tasks by contextualizing word embeddings for a better dynamic representation. Their proteinspecific versions, such as ProtBERT, generated dynamic protein sequence embeddings, which resulted in better performance for several
-
P4PC: A Portal for Bioinformatics Resources of piRNAs and circRNAs Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-02 Yajun Liu, Ru Li, Yulian Ding, Xin Hong Hei, Fang-Xiang Wu
Background: PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs) are two kinds of non-coding RNAs (ncRNAs) that play important roles in epigenetic regulation, transcriptional regulation, post-transcriptional regulation of many biological processes. Although there exist various resources, it is still challenging to select such resources for specific research projects on ncRNAs. Method: In order
-
Improved Hybrid Approach for Enhancing Protein Coding Regions Identification in DNA Sequences Curr. Bioinform. (IF 4.0) Pub Date : 2024-02-01 Emad S. Hassan, Ahmed M. Dessouky, Hesham Fathi, Gerges M. Salama, Ahmed S. Oshaba, Atef El-Emary, Fathi E. Abd El‑Samie
Introduction: Identifying and predicting protein-coding regions within DNA sequences play a pivotal role in genomic research. This paper introduces an approach for identifying proteincoding regions in DNA sequences, employing a hybrid methodology that combines a digital bandpass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Specifically, the Haar
-
Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-29 Haiping Zhang, Konda Mani Saravanan
Artificial Intelligence is a field within computer science that endeavors to replicate the intricate structures and operational mechanisms inherent in the human brain. Machine learning is a subfield of artificial intelligence that focuses on developing models by analyzing training data. Deep learning is a distinct subfield within artificial intelligence, characterized by using models that depict geometric
-
FMDVSerPred: A Novel Computational Solution for Foot-and-mouth Disease Virus Classification and Serotype Prediction Prevalent in Asia using VP1 Nucleotide Sequence Data Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-29 Samarendra Das, Soumen Pal, Samyak Mahapatra, Jitendra K. Biswal, Sukanta K. Pradhan, Aditya P. Sahoo, Rabindra Prasad Singh
Background: Three serotypes of Foot-and-mouth disease (FMD) virus have been circulating in Asia, which are commonly identified by serological assays. Such tests are timeconsuming and also need a bio-containment facility for execution of the assays. To the best of our knowledge, no computational solution is available in the literature to predict the FMD virus serotypes. Thus, this necessitates the urgent
-
Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-29 Lei Chen, Linyang Li
Background:: Drug repositioning now is an important research area in drug discovery as it can accelerate the procedures of discovering novel effects of existing drugs. However, it is challenging to screen out possible effects for given drugs. Designing computational methods are a quick and cheap way to complete this task. Most existing computational methods infer the relationships between drugs and
-
Prospects of Identifying Alternative Splicing Events from Single-Cell RNA Sequencing Data Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-26 Jiacheng Wang, Lei Yuan
Background: The advent of single-cell RNA sequencing (scRNA-seq) technology has offered unprecedented opportunities to unravel cellular heterogeneity and functions. Yet, despite its success in unraveling gene expression heterogeneity, accurately identifying and interpreting alternative splicing events from scRNA-seq data remains a formidable challenge. With advancing technology and algorithmic innovations
-
Application of Deep Learning Neural Networks in Computer-aided Drug Discovery: A Review Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-25 Jay Shree Mathivanan, Victor Violet Dhayabaran, Mary Rajathei David, Muthugobal Bagayalakshmi Karuna Nidhi, Karuppasamy Muthuvel Prasath, Suvaiyarasan Suvaithenamudhan
: Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques
-
Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-24 Chandrashekar K, Vidya Niranjan, Adarsh Vishal, Anagha S Setlur
: In the current state of genomics and biomedical research, the utilization of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have emerged as paradigm shifters. While traditional NGS DNA and RNA sequencing analysis pipelines have been sound in decoding genetic information, the sequencing data’s volume and complexity have surged. There is a demand for more efficient and accurate
-
Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-21 Yaxuan Cui, Leyi Wei, Ruheng Wang, Xiucai Ye, Tetsuya Sakurai
Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability
-
Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-17 Xianzhe Zou, Chen Zhang, Mingyan Tang, Lei Deng
Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to
-
Identification of Mitophagy-Related Genes in Sepsis Curr. Bioinform. (IF 4.0) Pub Date : 2024-01-05 Xiao-Yan Zeng, Min Zhang, Si-Jing Liao, Yong Wang, Ying-Bo Ren, Run Li, Tian-Mei Li, An-Qiong Mao, Guang-Zhen Li, Ying Zhang
Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated
-
SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-22 Prabina Kumar Meher, Siddhartha Hati, Tanmaya Kumar Sahu, Upendra Pradhan, Ajit Gupta, Surya Narayan Rath
Background: Root is a desirable trait for modern plant breeding programs, as the roots play a pivotal role in the growth and development of plants. Therefore, identification of the genes governing the root traits is an essential research component. With regard to the identification of root-associated genes/proteins, the existing wet-lab experiments are resource intensive and the gene expression studies
-
A Novel Natural Graph for Efficient Clustering of Virus Genome Sequences Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-15 Harris Song, Nan Sun, Wenping Yu, Stephen Yau
Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or
-
Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-07 Sujata Dash, Sourav Kumar Giri, Subhendu Kumar Pani, Saurav Mallik, Mingqiang Wang, Hong Qin
Background:: With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective:: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are
-
A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-04 Farnoosh Barneh, Ahmad Nazarian, Rezvan Mousavi Nadoshan, Kamran Pooshang Bagheri
Background: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective: In this study, we proposed a novel filtration
-
Identifying Pathological Myopia Associated Genes with A Random Walk-Based Method in Protein-Protein Interaction Network Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-04 Jiyu Zhang, Tao Huang, Qiao Sun, Jian Zhang
Background: Pathological myopia, a severe variant of myopia, extends beyond the typical refractive error associated with nearsightedness. While the condition has a strong genetic component, the intricate mechanisms of inheritance remain elusive. Some genes have been associated with the development of pathological myopia, but their exact roles are not fully understood. Objective: This study aimed to
-
Metabolomics: Recent Advances and Future Prospects Unveiled Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-04 Shweta Sharma, Garima Singh, Mymoona Akhter
: In the era of genomics, fueled by advanced technologies and analytical tools, metabo-lomics has become a vital component in biomedical research. Its significance spans various do-mains, encompassing biomarker identification, uncovering underlying mechanisms and pathways, as well as the exploration of new drug targets and precision medicine. This article presents a com-prehensive overview of the latest
-
Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-04 Ying Liang, Suhui Li, Xiya You, You Guo, Jianjun Tang
Background:: Protein lysine crotonylation (Kcr), a newly discovered important post-translational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmen-tal defects and malignant transformation. Objective:: Identifying Kcr sites is advantageous for the discovery of its
-
Discovering Microbe-disease Associations with Weighted Graph Convolution Networks and Taxonomy Common Tree Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-04 Jieqi Xing, Yu Shi, Xiaoquan Su, Shunyao Wu
Background:: Microbe-disease associations are integral to understanding complex dis-eases and their screening procedures. Objective:: While numerous computational methods have been developed to detect these associa-tions, their performance remains limited due to inadequate utilization of weighted inherent similari-ties and microbial taxonomy hierarchy. To address this limitation, we have introduced
-
Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-01 Xiaojing Zhu, Jiaxing Zhang, Zixin Zhang, Hongyan Yuan, Aimin xie, Nan Zhang, Mingwei wang, Minghui jiang, Yanqi Xiao, Hao Wang, Xing Wang, Yan Xu
Aims: Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC. Background: The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular
-
iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-01 Muhammad Shujaat, Sunggoo Yoo, Hilal Tayara, Kil To Chong
Background and Objective: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine
-
Prediction of Super-enhancers Based on Mean-shift Undersampling Curr. Bioinform. (IF 4.0) Pub Date : 2023-12-01 Han Cheng, Shumei Ding, Cangzhi Jia
Background:: Super-enhancers are clusters of enhancers defined based on the binding occupancy of master transcription factors, chromatin regulators, or chromatin marks. It has been reported that super-enhancers are transcriptionally more active and cell-type-specific than regular enhancers. Therefore, it is necessary to identify super-enhancers from regular enhancers. A variety of computational methods
-
Prediction of Plant Ubiquitylation Proteins and Sites by Fusing Multiple Features Curr. Bioinform. (IF 4.0) Pub Date : 2023-11-20 Meng-Yue Guan, Wang-Ren Qiu, Qian-Kun Wang, Xuan Xiao
Introduction: Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have
-
TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet Curr. Bioinform. (IF 4.0) Pub Date : 2023-11-20 Tao Zhang, Leying Pan, Qiang Yang, Guoping Yang, Nan Han, Shaojie Qiao
Background: Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast
-
SCV Filter: A Hybrid Deep Learning Model for SARS-CoV-2 Variants Classification Curr. Bioinform. (IF 4.0) Pub Date : 2023-11-16 Han Wang, Jingyang Gao
Background: The high mutability of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) makes it easy for mutations to occur during transmission. As the epidemic continues to develop, several mutated strains have been produced. Researchers worldwide are working on the effective identification of SARS-CoV-2. Objective: In this paper, we propose a new deep learning method that can effectively
-
Computational Methods for Functional Characterization of lncRNAS in Human Diseases: A Focus on Co-Expression Networks Curr. Bioinform. (IF 4.0) Pub Date : 2023-11-06 Prabhash Jha, Miguel Barbeiro, Adrien Lupieri, Elena Aikawa, Shizuka Uchida, Masanori Aikawa
Treatment of many human diseases involves small-molecule drugs.Some target proteins, however, are not druggable with traditional strategies. Innovative RNA-targeted therapeutics may overcome such a challenge. Long noncoding RNAs (lncRNAs) are transcribed RNAs that do not translate into proteins. Their ability to interact with DNA, RNA, microRNAs (miRNAs), and proteins makes them an interesting target
-
Prediction of DNA-binding Sites in Transcriptions Factor in Fur-like Proteins Using Machine Learning and Molecular Descriptors Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-27 Jessica Lara Muñoz, José Antonio Reyes-Suárez, Felipe Besoain, Mauricio Arenas-Salinas
Introduction: Transcription factors are of great interest in biotechnology due to their key role in the regulation of gene expression. One of the most important transcription factors in gramnegative bacteria is Fur, a global regulator studied as a therapeutic target for the design of antibacterial agents. Its DNA-binding domain, which contains a helix-turn-helix motif, is one of its most relevant features
-
NaProGraph: Network Analyzer for Interactions between Nucleic Acids and Proteins Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-20 Sajjad nematzadeh, Nizamettin Aydin, Zeyneb Kurt, Mahsa Torkamanian-Afshar
Background: Interactions of RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. Methods: This study proposes an online network provider tool (NaProGraph) that offers an intuitive
-
Toxicity Prediction for Immune Thrombocytopenia Caused by Drugs Based on Logistic Regression with Feature Importance Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-19 Osphanie Mentari, Muhammad Shujaat, Hilal Tayara, Kil To Chong
Background: One of the problems in drug discovery that can be solved by artificial intelligence is toxicity prediction. In drug-induced immune thrombocytopenia, toxicity can arise in patients after five to ten days by significant bleeding caused by drugdependent antibodies. In clinical trials, when this condition occurs, all the drugs consumed by patients should be stopped, although sometimes this
-
Revealing ANXA6 as a Novel Autophagy-related Target for Pre-eclampsia Based on the Machine Learning Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-18 Baoping Zhu, Huizhen Geng, Fan Yang, Yanxin Wu, Tiefeng Cao, Dongyu Wang, Zilian Wang
Background: Preeclampsia (PE) is a severe pregnancy complication associated with autophagy. Objective: This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning. Methods: GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yield
-
Representation Learning of Biological Concepts: A Systematic Review Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-17 Yuntao Yang, Xu Zuo, Avisha Das, Hua Xu, Wenjin Zheng
Objective: Representation learning in the context of biological concepts involves acquiring their numerical representations through various sources of biological information, such as sequences, interactions, and literature. This study has conducted a comprehensive systematic review by analyzing both quantitative and qualitative data to provide an overview of this field. Methods: Our systematic review
-
QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-16 Jie Gao, Qiming Fu, Jiacheng Sun, Yunzhe Wang, Youbing Xia, You Lu, Hongjie Wu, Jianping Chen
Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single
-
Network Propagation-based Identification of Oligometastatic Biomarkers in Metastatic Colorectal Cancer Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-16 Qing Jin, Kexin Yu, Xianze Zhang, Diwei Huo, Denan Zhang, Lei Liu, Hongbo Xie, Binhua Liang, Xiujie Chen
Background: The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial. Objective:
-
Full-length PacBio Amplicon Sequencing to Unveil RNA Editing Sites Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-16 Xiao-Lu Zhu, Ming-Ling Liao, Ya-Jie Zhu, Yun-Wei Dong
Background: RNA editing enriches post-transcriptional sequence changes. Currently detecting RNA editing sites is mostly based on the Sanger sequencing platform and second-generation sequencing. However, detection with Sanger sequencing is limited by the disturbing background peaks using the direct sequencing method and the clone number using the clone sequencing method, while second-generation sequencing
-
A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-11 Ishleen Kaur, Tanvir Ahmad, M.N. Doja
Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because
-
Bioinformatics Perspective of Drug Repurposing Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-10 Binita Patel, Brijesh Gelat, Mehul Soni, Pooja Rathaur, Kaid Johar SR
Different diseases can be treated with various therapeutic agents. Drug discovery aims to find potential molecules for existing and emerging diseases. However, factors, such as increasing development cost, generic competition due to the patent expiry of several drugs, increase in conservative regulatory policies, and insufficient breakthrough innovations impairs the development of new drugs and the
-
MSSD: An Efficient Method for Constructing Accurate and Stable Phylogenetic Networks by Merging Subtrees of Equal Depth Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-09 Jiajie Xing, Xu Song, Meiju Yu, Juan Wang, Jing Yu
Background:: Systematic phylogenetic networks are essential for studying the evolutionary relationships and diversity among species. These networks are particularly important for capturing non-tree-like processes resulting from reticulate evolutionary events. However, existing methods for constructing phylogenetic networks are influenced by the order of inputs. The different orders can lead to inconsistent
-
DMR_Kmeans: Identifying Differentially Methylated Regions Based on k-means Clustering and Read Methylation Haplotype Filtering Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-06 Xiaoqing Peng, Wanxin Cui, Xiangyan Kong, Yuannan Huang, Ji Li
Introduction: Differentially methylated regions (DMRs), including tissue-specific DMRs and disease-specific DMRs, can be used in revealing the mechanisms of gene regulation and screening diseases. Up until now, many methods have been proposed to detect DMRs from bisulfite sequencing data. In these methods, differentially methylated CpG sites and DMRs are usually identified based on statistical tests
-
A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction Curr. Bioinform. (IF 4.0) Pub Date : 2023-10-06 Meng-Meng Wei, Chang-Qing Yu, Li-Ping Li, Zhu-Hong You, Lei-Wang
Background: LncRNA is not only involved in the regulation of the biological functions of protein-coding genes, but its dysfunction is also associated with the occurrence and progression of various diseases. Various studies have shown that an in-depth understanding of the mechanism of action of lncRNA is of great significance for disease treatment. However, traditional wet testing is time-consuming