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Journals IEEE/ACM Transactions on Compu...

IEEE/ACM Transactions on Computational Biology and Bioinformatics

https://read.qxmd.com/read/38536676/exploring-the-knowledge-of-an-outstanding-protein-to-protein-interaction-transformer
#1
JOURNAL ARTICLE
Sen Yang, Peng Cheng, Lingli Ju, Yang Liu, Dawei Feng, Shengqi Wang
Protein-to-protein interaction (PPI) prediction aims to predict whether two given proteins interact or not. Compared with traditional experimental methods of high cost and low efficiency, the current deep learning based approach makes it possible to discover massive potential PPIs from large-scale databases. However, deep PPI prediction models perform poorly on unseen species, as their proteins are not in the training set. Targetting on this issue, the paper first proposes PPITrans, a Transformer based PPI prediction model that exploits a language model pre-trained on proteins to conduct binary PPI prediction...
March 27, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38536675/a-novel-method-for-targeted-identification-of-essential-proteins-by-integrating-chemical-reaction-optimization-and-naive-bayes-model
#2
JOURNAL ARTICLE
Wenya Yang, Sai Zou, Hongfeng Gao, Lei Wang, Wei Ni
Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins...
March 27, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38526906/deep-spatio-temporal-network-for-low-snr-cryo-em-movie-frame-enhancement
#3
JOURNAL ARTICLE
Xiaoya Chong, Howard Leung, Qing Li, Jianhua Yao, Niyun Zhou
Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks. However, they are designed for natural images with high SNR. Meanwhile, the lack of ground truth in cryo-EM movie seems to be one major limiting factor of the progress...
March 25, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38517710/feature-fusion-gan-based-virtual-staining-on-plant-microscopy-images
#4
JOURNAL ARTICLE
Sumona Biswas, Shovan Barma
Virtual staining of microscopy specimens using GAN-based methods could resolve critical concerns of manual staining process as displayed in recent studies on histopathology images. However, most of these works use basic-GAN framework ignoring microscopy image characteristics and their performance were evaluated based on structural and error statistics (SSIM and PSNR) between synthetic and ground-truth without considering any color space although virtual staining deals with color transformation. Besides, major aspects of staining, like color, contrast, focus, image-realness etc...
March 22, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38507390/functional-neural-networks-for-high-dimensional-genetic-data-analysis
#5
JOURNAL ARTICLE
Shan Zhang, Yuan Zhou, Pei Geng, Qing Lu
Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. Machine learning methods, such as artificial neural networks (ANN), play a central role in modern AI technology. While ANN also holds great promise for human genetic research, the high-dimensional genetic data and complex genetic structure bring tremendous challenges. The vast majority of genetic variants on the genome have small or no effects on diseases, and fitting ANN on a large number of variants without considering the underlying genetic structure (e...
March 20, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38498764/the-delfos-platform-a-conceptual-model-based-solution-for-the-enhancement-of-precision-medicine
#6
JOURNAL ARTICLE
Ana Leon, Alberto Garcia S, Jose Fabian Reyes Roman, Mireia Costa, Oscar Pastor
The use in the clinical practice of the vast amount of genomic data generated by current sequencing technologies constitutes a bottleneck for the progress of Precision Medicine (PM). Various problems inherent to the genomics domain (i.e., dispersion, heterogeneity, discrepancies, lack of standardization, and data quality issues) remain unsolved. In this paper, we present the Delfos platform, a conceptual model-based solution developed following a rigorous methodological and ontological background, whose main aim is to minimize the impact of these problems when transferring the research results to clinical practice...
March 19, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38498765/tdffm-transformer-and-deep-forest-fusion-model-for-predicting-coronavirus-3c-like-protease-cleavage-sites
#7
JOURNAL ARTICLE
Qingsong Wang, Ruiquan Ge, Changmiao Wang, Ahmed Elazab, Qiming Fang, Renfeng Zhang
COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C- like protease of the coronavirus (denoted as 3CLpro ) is instrumental in the viral replication process. Precise delineation of 3CLpro cleavage sites is imperative for elucidating the transmission dynamics of SARS-CoV-2. While machine learning tools have been deployed to identify potential 3CLpro cleavage sites, these existing methods often fall short in terms of accuracy...
March 18, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38498763/flupmt-prediction-of-predominant-strains-of-influenza-a-viruses-via-multi-task-learning
#8
JOURNAL ARTICLE
Changfeng Cai, Jianghui Li, Yuanling Xia, Weihua Li
Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vaccine strain is generally based on the assessment of the current predominant strains. Nevertheless, the process of vaccine production and distribution is very time-consuming, leaving a window for the emergence of new variants that could decrease vaccine effectiveness, so predictions of influenza A virus evolution can inform vaccine evaluation and selection...
March 18, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38498762/network-modeling-and-control-of-dynamic-disease-pathways-review-and-perspectives
#9
JOURNAL ARTICLE
Yen-Che Hsiao, Abhishek Dutta
Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses...
March 18, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38470596/boundary-aware-dual-biaffine-model-for-sequential-sentence-classification-in-biomedical-documents
#10
JOURNAL ARTICLE
Junwen Duan, Huai Guo, Han Jiang, Fei Guo, Jianxin Wang
Assigning appropriate rhetorical roles, such as "background," "intervention," and "outcome," to sentences in biomedical documents can streamline the process for physicians to locate evidence and resources for medical treatment and decision-making. While sequence labeling and span-based methods are frequently employed for this task, the former disregards a document's semantic structure, resulting in a lack of semantic coherence across continuous sentences. Span-based approaches, on the other hand, either necessitate the enumeration of all potential spans, which can be time-consuming, or may lead to the misclassification of sentences over extended spans...
March 12, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38470595/chinese-emr-named-entity-recognition-using-fused-label-relations-based-on-machine-reading-comprehension-framework
#11
JOURNAL ARTICLE
Junwen Duan, Shuyue Liu, Xincheng Liao, Feng Gong, Hailin Yue, Jianxin Wang
Chinese electronic medical records (EMR) presents significant challenges for named entity recognition (NER) due to their specialized nature, unique language features, and diverse expressions. Traditionally, NER is treated as a sequence labeling task, where each token is assigned a label. Recent research has reframed NER within the machine reading comprehension (MRC) framework, extracting entities in a question-answer format, achieving state-of-the-art performance. However, these MRC-based methods have a significant limitation: they extract entities of various types independently, ignoring their interrelations...
March 12, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38451771/vipra-haplo-de-novo-reconstruction-of-viral-populations-using-paired-end-sequencing-data
#12
JOURNAL ARTICLE
Weiling Li, Raunaq Malhotra, Steven Wu, Manjari Jha, Allen Rodrigo, Mary Poss, Raj Acharya
We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH [1] based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations...
March 7, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38451770/tsvm-transfer-support-vector-machine-for-predicting-mpra-validated-regulatory-variants
#13
JOURNAL ARTICLE
Minglie Li, Shusen Zhou, Tong Liu, Chanjuan Liu, Mujun Zang, Qingjun Wang
Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants makes it difficult to develop an effective supervised learning model. Therefore, improving the accuracy of predicting non-coding causal variants has become critical. This study aims to build a transfer learning-based machine learning method for predicting regulatory variants to overcome the problem of limited sample size...
March 7, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38451769/pprtgi-a-personalized-pagerank-graph-neural-network-for-tf-target-gene-interaction-detection
#14
JOURNAL ARTICLE
Ke Ma, Jiawei Li, Mengyuan Zhao, Ibrahim Zamit, Bin Lin, Fei Guo, Jijun Tang
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for understanding cellular processes and analyzing disease molecular mechanisms. Computational approaches can be challenging to employ when attempting to predict potential interactions between TFs and target genes. In this paper, we present a novel graph model (PPRTGI) for detecting TF-target gene interactions using DNA sequence features...
March 7, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38446654/sglmda-a-subgraph-learning-based-method-for-mirna-disease-association-prediction
#15
JOURNAL ARTICLE
Cunmei Ji, Ning Yu, Yutian Wang, Jiancheng Ni, Chunhou Zheng
MicroRNAs (miRNA) are endogenous non-coding RNAs, typically around 23 nucleotides in length. Many miRNAs have been founded to play crucial roles in gene regulation though post-transcriptional repression in animals. Existing studies suggest that the dysregulation of miRNA is closely associated with many human diseases. Discovering novel associations between miRNAs and diseases is essential for advancing our understanding of disease pathogenesis at molecular level. However, experimental validation is time-consuming and expensive...
March 6, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38442065/accurate-annotation-for-differentiating-and-imbalanced-cell-types-in-single-cell-chromatin-accessibility-data
#16
JOURNAL ARTICLE
Yuhang Jia, Siyu Li, Rui Jiang, Shengquan Chen
Rapid advances in single-cell chromatin accessibility sequencing (scCAS) technologies have enabled the characterization of epigenomic heterogeneity and increased the demand for automatic annotation of cell types. However, there are few computational methods tailored for cell type annotation in scCAS data and the existing methods perform poorly for differentiating and imbalanced cell types. Here, we propose CASCADE, a novel annotation method based on simulation- and denoising-based strategies. With comprehensive experiments on a number of scCAS datasets, we showed that CASCADE can effectively distinguish the patterns of different cell types and mitigate the effect of high noise levels, and thus achieve significantly better annotation performance for differentiating and imbalanced cell types...
March 5, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38427545/evasive-spike-variants-elucidate-the-preservation-of-t-cell-immune-response-to-the-sars-cov-2-omicron-variant
#17
JOURNAL ARTICLE
Arnav Solanki, James Cornette, Julia Udell, George Vasmatzis, Marc Riedel
The Omicron variants boast the highest infectivity rates among all SARS-CoV-2 variants. Despite their lower disease severity, they can reinfect COVID-19 patients and infect vaccinated individuals as well. The high number of mutations in these variants render them resistant to antibodies that otherwise neutralize the spike protein of the original SARS-CoV-2 spike protein. Recent research has shown that despite its strong immune evasion, Omicron still induces strong T Cell responses similar to the original variant...
March 1, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38427544/computational-prediction-of-potential-vaccine-candidates-from-trna-encoded-peptides-trep-using-a-bioinformatic-workflow-and-molecular-dynamics-validations
#18
JOURNAL ARTICLE
Pallavi M Shanthappa, Neeraj Verma, Anu George, Pawan K Dhar, Prashanth Athri
Transfer RNAs (tRNA) are non-coding RNAs. Encouraged by biological applications discovered for peptides derived from other non-coding genomic regions, we explore the possibility of deriving epitope-based vaccines from tRNA encoded peptides (tREP) in this study. Epitope-based vaccines have been identified as an effective strategy to mitigate safety and specificity concerns observed in vaccine development. In this study, we explore the potential of tREP as a source for epitope-based vaccines for virus pathogens...
March 1, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38408003/parallel-algorithm-for-discovering-and-comparing-three-dimensional-proteins-patterns
#19
JOURNAL ARTICLE
Alejandro Valdes-Jimenez, Miguel Reyes-Parada, Gabriel Nunez-Vivanco, Daniel Jimienez-Gonzalez
Identifying conserved (similar) three-dimensional patterns among a set of proteins can be helpful for the rational design of polypharmacological drugs. Some available tools allow this identification from a limited perspective, only considering the available information, such as known binding sites or previously annotated structural motifs. Thus, these approaches do not look for similarities among all putative orthosteric and or allosteric bindings sites between protein structures. To overcome this tech-weakness Geomfinder was developed, an algorithm for the estimation of similarities between all pairs of three-dimensional amino acids patterns detected in any two given protein structures, which works without information about their known patterns...
February 26, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38381638/interpretable-prediction-of-sars-cov-2-epitope-specific-tcr-recognition-using-a-pre-trained-protein-language-model
#20
JOURNAL ARTICLE
Sunyong Yoo, Myeonghyeon Jeong, Subhin Seomun, Kiseong Kim, Youngmahn Han
The emergence of the novel coronavirus, designated as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a significant threat to public health worldwide. There has been progress in reducing hospitalizations and deaths due to SARS-CoV-2. However, challenges stem from the emergence of SARS-CoV-2 variants, which exhibit high transmission rates, increased disease severity, and the ability to evade humoral immunity. Epitope-specific T-cell receptor (TCR) recognition is key in determining the T-cell immunogenicity for SARS-CoV-2 epitopes...
February 21, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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