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EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-02-26 Jujuan Zhuang, Wanquan Gao, Rui Su
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of
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Predictive Recognition of DNA-binding Proteins Based on Pre-trained Language Model BERT J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-01-23 Yue Ma, Yongzhen Pei, Changguo Li
Identifying proteins is crucial for disease diagnosis and treatment. With the increase of known proteins, large-scale batch predictions are essential. However, traditional biological experiments being time-consuming and expensive are difficult to accomplish this task efficiently. Nevertheless, deep learning algorithms based on big data analysis have manifested potential in this aspect. In recent years
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Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-01-23 Jiadi Zhu, Youlong Yang
Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix
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Small groups in multidimensional feature space: Two examples of supervised two-group classification from biomedicine J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-01-10 Dmitriy Karpenko, Aleksei Bigildeev
Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets
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Algorithms for the Uniqueness of the Longest Common Subsequence J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-01-10 Yue Wang
Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are
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CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2024-01-10 Chengyou Li, Shiqiang Fan, Haiyong Zhao, Xiaotong Liu
Copy number variation (CNV), as a type of genomic structural variation, accounts for a large proportion of structural variation and is related to the pathogenesis and susceptibility to some human diseases, playing an important role in the development and change of human diseases. The development of next-generation sequencing technology (NGS) provides strong support for the design of CNV detection algorithms
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CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-10-28 Ying Zeng,Zheming Yuan,Yuan Chen,Ying Hu
O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable
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AAindex-PPII: Predicting polyproline type II helix structure based on amino acid indexes with an improved BiGRU-TextCNN model. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-10-28 Jiasheng He,Shun Zhang,Chun Fang
The polyproline-II (PPII) structure domain is crucial in organisms' signal transduction, transcription, cell metabolism, and immune response. It is also a critical structural domain for specific vital disease-associated proteins. Recognizing PPII is essential for understanding protein structure and function. To accurately predict PPII in proteins, we propose a novel method, AAindex-PPII, which only
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iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-10-27 Zizheng Yu,Zhijian Yin,Hongliang Zou
Various diseases, including Huntington's disease, Alzheimer's disease, and Parkinson's disease, have been reported to be linked to amyloid. Therefore, it is crucial to distinguish amyloid from non-amyloid proteins or peptides. While experimental approaches are typically preferred, they are costly and time-consuming. In this study, we have developed a machine learning framework called iAMY-RECMFF to
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Analyzing omics data by feature combinations based on kernel functions. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-10-18 Chao Li,Tianxiang Wang,Xiaohui Lin
Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, fixed pattern for all feature pairs, such as linear combination. To identify the appropriate combination between two features and evaluate feature combination
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Methods for cell-type annotation on scRNA-seq data: A recent overview. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-09-23 Konstantinos Lazaros,Panagiotis Vlamos,Aristidis G Vrahatis
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has
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Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-09-08 Fuyan Hu,Bifeng Chen,Qing Wang,Zhiyuan Yang,Man Chu
Cancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly
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A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-09-08 Carlos E M Relvas,Asuka Nakata,Guoan Chen,David G Beer,Noriko Gotoh,Andre Fujita
Usually, the clustering process is the first step in several data analyses. Clustering allows identify patterns we did not note before and helps raise new hypotheses. However, one challenge when analyzing empirical data is the presence of covariates, which may mask the obtained clustering structure. For example, suppose we are interested in clustering a set of individuals into controls and cancer patients
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Facilitating the drug repurposing with iC/E strategy: A practice on novel nNOS inhibitor discovery. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-09-06 Zhaoyang Hu,Qingsen Liu,Zhong Ni
Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually
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DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-08-24 Hayat Ali Shah,Juan Liu,Zhihui Yang,Feng Yang,Qiang Zhang,Jing Feng
Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions
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Local RNA folding revisited J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-07-28 Maria Waldl, Thomas Spicher, Ronny Lorenz, Irene K. Beckmann, Ivo L. Hofacker, Sarah Von Löhneysen, Peter F. Stadler
Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages
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Expansin gene family database: A comprehensive bioinformatics resource for plant expansin multigene family J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-30 Büşra Özkan Kök, Yasemin Celik Altunoglu, Ali Burak Öncül, Abdulkadir Karaci, Mehmet Cengiz Baloglu
Expansins, which are plant cell wall loosening proteins associated with cell growth, have been identified as a multigene family. Plant expansin proteins are an important family that functions in cell growth and many of developmental processes including wall relaxation, fruit softening, abscission, seed germination, mycorrhiza and root nodule formation, biotic and abiotic stress resistance, invasion
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Overlapping group screening for binary cancer classification with TCGA high-dimensional genomic data J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-22 Jie-Huei Wang, Yi-Hau Chen
Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients’ survival. Since disease developments involve complex interplay among multiple factors such as gene–gene interactions, cancer classifications based on microarray gene
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Drug synergy model for malignant diseases using deep learning J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-22 Pooja Rani, Kamlesh Dutta, Vijay Kumar
Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when compared to single-drug doses. Thus, it has attained significant interest from academics and pharmaceutical organizations. Due to the enormous combinatorial search space, it is impossible to experimentally validate every conceivable combination
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Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-15 Qi-Shi Song, Hai-Jun Wu, Qian Lin, Yu-Kai Tang
Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring
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The mechanism accounting for DNA damage strength modulation of p53 dynamical properties J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-15 Aiqing Ma, Xianhua Dai
The P53 protein levels exhibit a series of pulses in response to DNA double-stranded breaks (DSBs). However, the mechanism regarding how damage strength regulates physical parameters of p53 pulses remains to be elucidated. This paper established two mathematical models translating the mechanism of p53 dynamics in response to DSBs; the two models can reproduce many results observed in the experiments
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Integrating temporal and spatial variabilities for identifying ion binding proteins in phage J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-06-15 Hongliang Zou, Zizheng Yu, Zhijian Yin
Recent studies reported that ion binding proteins (IBPs) in phage play a key role in developing drugs to treat diseases caused by drug-resistant bacteria. Therefore, correct recognition of IBPs is an urgent task, which is beneficial for understanding their biological functions. To explore this issue, a new computational model was developed to identify IBPs in this study. First, we used the physicochemical
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Antimicrobial peptides recognition using weighted physicochemical property encoding J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-04-29 Standa Na, Dhammika Leshan Wannigama, Thammakorn Saethang
Antimicrobial resistance is a major public health concern. Antimicrobial peptides (AMPs) are one of the host defense mechanisms responding efficiently against multidrug-resistant microbes. Since the process of screening AMPs from a large number of peptides is still high-priced and time-consuming, the development of a precise and rapid computer-aided tool is essential for preliminary AMPs selection
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Rearrangement distance with reversals, indels, and moves in intergenic regions on signed and unsigned permutations J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-04-27 Klairton Lima Brito, Andre Rodrigues Oliveira, Alexsandro Oliveira Alexandrino, Ulisses Dias, Zanoni Dias
Genome rearrangement events are widely used to estimate a minimum-size sequence of mutations capable of transforming a genome into another. The length of this sequence is called distance, and determining it is the main goal in genome rearrangement distance problems. Problems in the genome rearrangement field differ regarding the set of rearrangement events allowed and the genome representation. In
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Integrated in silico–in vitro rational design of oncogenic EGFR-derived specific monoclonal antibody-binding peptide mimotopes J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-04-27 Ke Chen, Lili Ge, Guorui Liu
Human epidermal growth factor receptor (EGFR) is strongly associated with malignant proliferation and has been established as an attractive therapeutic target of diverse cancers and used as a significant biomarker for tumor diagnosis. Over the past decades, a variety of monoclonal antibodies (mAbs) have been successfully developed to specifically recognize the third subdomain (TSD) of EGFR extracellular
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ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-31 Hui-Ling Huang, Chong-Heng Weng, Torbjörn E. M. Nordling, Yi-Fan Liou
Motivation: The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI)
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Obstacles to effective model deployment in healthcare J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-18 Wei Xin Chan, Limsoon Wong
Despite an exponential increase in publications on clinical prediction models over recent years, the number of models deployed in clinical practice remains fairly limited. In this paper, we identify common obstacles that impede effective deployment of prediction models in healthcare, and investigate their underlying causes. We observe a key underlying cause behind most obstacles — the improper development
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NuKit: A deep learning platform for fast nucleus segmentation of histopathological images J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-11 Ching-Nung Lin, Christine H. Chung, Aik Choon Tan
Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods have demonstrated to outperform previous intensity- or pattern-based methods. However, the heavy computation of deep learning provides impression of lagging
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Numerical study of chronic hepatitis B infection using Marchuk–Petrov model J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-09 Michael Khristichenko, Yuri Nechepurenko, Dmitry Grebennikov, Gennady Bocharov
In this work, we briefly describe our technology developed for computing periodic solutions of time-delay systems and discuss the results of computing periodic solutions for the Marchuk–Petrov model with parameter values, corresponding to hepatitis B infection. We identified the regions in the model parameter space in which an oscillatory dynamics in the form of periodic solutions exists. The respective
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RiRPSSP: A unified deep learning method for prediction of regular and irregular protein secondary structures J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-09 Mukhtar Ahmad Sofi, M. Arif Wani
Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino acids consist of helices and sheets, whereas the remaining amino acids represent irregular SSs. β-turns and γ-turns are the most abundant irregular SSs
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A novel method for predicting DNA N4-methylcytosine sites based on deep forest algorithm J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-09 Yonglin Zhang, Mei Hu, Qi Mo, Wenli Gan, Jiesi Luo
N4-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput
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Evaluating network-based missing protein prediction using p-values, Bayes Factors, and probabilities J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-09 Wilson Wen Bin Goh, Weijia Kong, Limsoon Wong
Some prediction methods use probability to rank their predictions, while some other prediction methods do not rank their predictions and instead use p-values to support their predictions. This disparity renders direct cross-comparison of these two kinds of methods difficult. In particular, approaches such as the Bayes Factor upper Bound (BFB) for p-value conversion may not make correct assumptions
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Testing and improving the performance of protein thermostability predictors for the engineering of cellulases J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-08 Anna Dotsenko, Jury Denisenko, Dmitrii Osipov, Aleksandra Rozhkova, Ivan Zorov, Arkady Sinitsyn
Thermostability of cellulases can be increased through amino acid substitutions and by protein engineering with predictors of protein thermostability. We have carried out a systematic analysis of the performance of 18 predictors for the engineering of cellulases. The predictors were PoPMuSiC, HoTMuSiC, I-Mutant 2.0, I-Mutant Suite, PremPS, Hotspot, Maestroweb, DynaMut, ENCoM (ΔΔG and ΔΔSvib), mCSM
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A pharmacokinetic model based on the SSA-1DCNN-Attention method J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-03-08 Zi-yi He, Jie-yu Yang, Yong Li
To solve the problem of the lack of representativeness of the training set and the poor prediction accuracy due to the limited number of training samples when the machine learning method is used for the classification and prediction of pharmacokinetic indicators, this paper proposes a 1DCNN-Attention concentration prediction model optimized by the sparrow search algorithm (SSA). First, the SMOTE method
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In silico de novo drug design of a therapeutic peptide inhibitor against UBE2C in breast cancer J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-02-10 Andrea Mae Añonuevo, Marineil Gomez, Lemmuel L. Tayo
The World Health Organization (WHO) declared breast cancer (BC) as the most prevalent cancer in the world. With its prevalence and severity, there have been several breakthroughs in developing treatments for the disease. Targeted therapy treatments limit the damage done to healthy tissues. These targeted therapies are especially potent for luminal and HER-2 positive type breast cancer. However, for
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PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2023-02-10 Jayanta Pal, Sourav Saha, Bansibadan Maji, Dilip Kumar Bhattacharya
This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suitable alternative to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), which is inherently time-consuming in nature. Initially, principal component
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Quantification of the presence of enzymes in gelatin zymography using the Gini index J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-12-31 Adriana Laura López Lobato, Martha Lorena Avendaño Garrido, Héctor Gabriel Acosta Mesa, Clara Luz Sampieri, Víctor Hugo Sandoval Lozano
Gel zymography quantifies the activity of certain enzymes in tumor processes. These enzymes are widely used in medical diagnosis. In order to analyze them, experts classify the zymography spots into various classes according to their tonalities. This classification is done by visual analysis, which is what makes it a subjective process. This work proposes a methodology to carry out this classifications
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A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-12-27 Xin Huang, Benzhe Su, Xingyu Wang, Yang Zhou, Xinyu He, Bing Liu
Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To
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Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-12-14 Kano Hasegawa, Yoshitaka Moriwaki, Tohru Terada, Cao Wei, Kentaro Shimizu
In this study, we propose Feedback-AVPGAN, a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling approach that uses deep learning methods, comprises a generator and a discriminator. The generator is used to generate peptides; the generated
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Accounting for treatment during the development or validation of prediction models J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-12-14 Wei Xin Chan, Limsoon Wong
Clinical prediction models are widely used to predict adverse outcomes in patients, and are often employed to guide clinical decision-making. Clinical data typically consist of patients who received different treatments. Many prediction modeling studies fail to account for differences in patient treatment appropriately, which results in the development of prediction models that show poor accuracy and
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GCMCDTI: Graph convolutional autoencoder framework for predicting drug–target interactions based on matrix completion J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-11-09 Jing Li, Chen Zhang, Zhengwei Li, Ru Nie, Pengyong Han, Wenjia Yang, Hongmei Liao
Identification of potential drug–target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug–target interaction prediction. In this
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The impact of simulation time in predicting binding free energies using end-point approaches J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-11-09 Babak Sokouti, Siavoush Dastmalchi, Maryam Hamzeh-Mivehroud
The profound impact of in silico studies for a fast-paced drug discovery pipeline is undeniable for pharmaceutical community. The rational design of novel drug candidates necessitates considering optimization of their different aspects prior to synthesis and biological evaluations. The affinity prediction of small ligands to target of interest for rank-ordering the potential ligands is one of the most
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A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-10-27 Sarah-Laure Rincourt, Stefan Michiels, Damien Drubay
The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double
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Behavioral dynamics of bacteriophage gene regulatory networks J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-09-14 Gatis Melkus, Karlis Cerans, Karlis Freivalds, Lelde Lace, Darta Zajakina, Juris Viksna
We present hybrid system-based gene regulatory network models for lambda, HK022, and Mu bacteriophages together with dynamics analysis of the modeled networks. The proposed lambda phage model LPH2 is based on an earlier work and incorporates more recent biological assumptions about the underlying gene regulatory mechanism, HK022, and Mu phage models are new. All three models provide accurate representations
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COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-09-12 Wajid Arshad Abbasi, Asma Anjam, Sadia Khalil, Saiqa Andleeb, Maryum Bibi, Syed Ali Abbas
Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics
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Computational design and experimental confirmation of conformationally constrained peptides to compete with coactivators for pediatric PPARα by minimizing indirect readout effect J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-09-12 Caijie Gao, Xu Zhao, Jianrong Fan
The peroxisome proliferator-activated receptor-α (PPARα) is a member of PPAR nuclear receptor family, and its antagonists have been widely used to treat pediatric metabolic disorders. Traditional type-1 and type-2 PPARα antagonists are all small-molecule compounds that have been developed to target the ligand-binding site (LBS) of PPARα, which is not overlapped with the coactivator-interacting site
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TemporalGSSA: A numerically robust R-wrapper to facilitate computation of a metabolite-specific and simulation time-dependent trajectory from stochastic simulation algorithm (SSA)-generated datasets J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-08-08 Siddhartha Kundu
Whilst data on biochemical networks has increased several-fold, our comprehension of the underlying molecular biology is incomplete and inadequate. Simulation studies permit data collation from disparate time points and the imputed trajectories can provide valuable insights into the molecular biology of complex biochemical systems. Although, stochastic simulations are accurate, each run is an independent
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Introduction to Selected Papers from InCoB 2021. J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-08-03 Yun Zheng
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Transcriptomic meta-analysis reveals biomarker pairs and key pathways in Tetralogy of Fallot J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-08-03 Sona Charles, Sreekumar J, Jeyakumar Natarajan
Tetralogy of Fallot (TOF) is a cyanotic congenital condition contributed by genetic, epigenetic as well as environmental factors. We applied sparse machine learning algorithms to RNAseq and sRNAseq data to select the prospective biomarker candidates. Furthermore, we applied filtering techniques to identify a subset of biomarker pairs in TOF. Differential expression analysis disclosed 2757 genes and
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iRNA5hmC-HOC: High-order correlation information for identifying RNA 5-hydroxymethylcytosine modification J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-08-03 Hongliang Zou
RNA 5-hydroxymethylcytosine (5 hmC) is an important RNA modification, which plays vital role in several biological processes. Currently, it is a hot topic to identify 5 hmC sites due to its benefit in understanding its biological functions. Therefore, in this study, we developed a predictor called iRNA5 hmC-HOC, which is based on a high-order correlation information method to identify 5 hmC sites.
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bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-25 Ali Burak Öncül, Yüksel Çelik, Necdet Mehmet Ünel, Mehmet Cengiz Baloglu
The basic helix loop helix (bHLH) superfamily is a large and diverse protein family that plays a role in various vital functions in nearly all animals and plants. The bHLH proteins form one of the largest families of transcription factors found in plants that act as homo- or heterodimers to regulate the expression of their target genes. The bHLH transcription factor is involved in many aspects of plant
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An alignment-independent three-dimensional quantitative structure–activity relationship study on ron receptor tyrosine kinase inhibitors J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-18 Omid Zarei, Stéphane L. Raeppel, Maryam Hamzeh-Mivehroud
Recepteur d’Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure–activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent
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Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on extreme gradient boosting, targets and gene expression data J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-17 Mengqiu Sun, Shengnan She, Hengwei Chen, Jiaxi Cheng, Wei Ji, Dan Wang, Chunlai Feng
Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic
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Transforming OMIC features for classification using siamese convolutional networks J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-09 Qian Wang, Meiyu Duan, Yusi Fan, Shuai Liu, Yanjiao Ren, Lan Huang, Fengfeng Zhou
Modern biotechnologies have generated huge amount of OMIC data, among which transcriptomes and methylomes are two major OMIC types. Transcriptomes measure the expression levels of all the transcripts while methylomes depict the cytosine methylation levels across a genome. Both OMIC data types could be generated by array or sequencing. And some studies deliver many more features (the number of features
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Automated analysis of karyotype images J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-07 Ensieh Khazaei, Ala Emrany, Mostafa Tavassolipour, Foroozandeh Mahjoubi, Ahmad Ebrahimi, Seyed Abolfazl Motahari
Karyotype is a genetic test that is used for detection of chromosomal defects. In a karyotype test, an image is captured from chromosomes during the cell division. The captured images are then analyzed by cytogeneticists in order to detect possible chromosomal defects. In this paper, we have proposed an automated pipeline for analysis of karyotype images. There are three main steps for karyotype image
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Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-07-06 Nadia Tahiri, Andrey Veriga, Aleksandr Koshkarov, Boris Morozov
The evolutionary histories of genes are susceptible of differing greatly from each other which could be explained by evolutionary variations in horizontal gene transfers or biological recombinations. A phylogenetic tree would therefore represent the evolutionary history of each gene, which may present different patterns from the species tree that defines the main evolutionary patterns. In addition
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Flux balance network expansion predicts stage-specific human peri_implantation embryo metabolism J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-05-25 Andisheh Dadashi, Derek Martinez
Metabolism is an essential cellular process for the growth and maintenance of organisms. A better understanding of metabolism during embryogenesis may shed light on the developmental origins of human disease. Metabolic networks, however, are vastly complex with many redundant pathways and interconnected circuits. Thus, computational approaches serve as a practical solution for unraveling the genetic
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Prediction of nucleosome dynamic interval based on long–short-term memory network (LSTM) J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-05-21 Jianli Liu, Deliang Zhou, Wen Jin
Nucleosome localization is a dynamic process and consists of nucleosome dynamic intervals (NDIs). We preprocessed nucleosome sequence data as time series data (TSD) and developed a long short-term memory network (LSTM) model for training time series data (TSD; LSTM-TSD model) using iterative training and feature learning that predicts NDIs with high accuracy. Sn, Sp, Acc, and MCC of the obtained LSTM
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Denoising of scanning electron microscope images for biological ultrastructure enhancement J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-04-23 Sheng Chang, Lijun Shen, Linlin Li, Xi Chen, Hua Han
Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM
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Quantitative structure–activity relationship modeling reveals the minimal sequence requirement and amino acid preference of sirtuin-1’s deacetylation substrates in diabetes mellitus J. Bioinform. Comput. Biol. (IF 1.0) Pub Date : 2022-04-21 X. Shao, W. Kong, Y. Li, S. Zhang
Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase involved in multiple glucose metabolism pathways and plays an important role in the pathogenesis of diabetes mellitus (DM). The enzyme specifically recognizes its deacetylation substrates’ peptide segments containing a central acetyl-lysine residue as well as a number of amino acids flanking the central residue. In