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QNetDiff: a quantitative measurement of network rewiring BMC Bioinform. (IF 3.0) Pub Date : 2024-03-18 Shota Nose, Hirotsugu Shiroma, Takuji Yamada, Yushi Uno
Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the
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Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values BMC Bioinform. (IF 3.0) Pub Date : 2024-03-18 Seungjun Ahn, Somnath Datta
A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not
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Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation BMC Bioinform. (IF 3.0) Pub Date : 2024-03-16 Renjing Liu, Kun Qian, Xinwei He, Hongwei Li
The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity. To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning
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Using protein language models for protein interaction hot spot prediction with limited data BMC Bioinform. (IF 3.0) Pub Date : 2024-03-16 Karen Sargsyan, Carmay Lim
Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery
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NeuronBridge: an intuitive web application for neuronal morphology search across large data sets BMC Bioinform. (IF 3.0) Pub Date : 2024-03-15 Jody Clements, Cristian Goina, Philip M. Hubbard, Takashi Kawase, Donald J. Olbris, Hideo Otsuna, Robert Svirskas, Konrad Rokicki
Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in
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eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings BMC Bioinform. (IF 3.0) Pub Date : 2024-03-15 Kevin Z. Lin, Yixuan Qiu, Kathryn Roeder
Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals
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MetaTron: advancing biomedical annotation empowering relation annotation and collaboration BMC Bioinform. (IF 3.0) Pub Date : 2024-03-14 Ornella Irrera, Stefano Marchesin, Gianmaria Silvello
The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and
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StackDPP: a stacking ensemble based DNA-binding protein prediction model BMC Bioinform. (IF 3.0) Pub Date : 2024-03-14 Sheikh Hasib Ahmed, Dibyendu Brinto Bose, Rafi Khandoker, M Saifur Rahman
DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. In this work, we have proposed
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CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications BMC Bioinform. (IF 3.0) Pub Date : 2024-03-12 Simone Alessandri, Maria L. Ratto, Sergio Rabellino, Gabriele Piacenti, Sandro Gepiro Contaldo, Simone Pernice, Marco Beccuti, Raffaele A. Calogero, Luca Alessandri
The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address
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Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq BMC Bioinform. (IF 3.0) Pub Date : 2024-03-12 Jason R. Miller, Donald A. Adjeroh
Parent-of-origin allele-specific gene expression (ASE) can be detected in interspecies hybrids by virtue of RNA sequence variants between the parental haplotypes. ASE is detectable by differential expression analysis (DEA) applied to the counts of RNA-seq read pairs aligned to parental references, but aligners do not always choose the correct parental reference. We used public data for species that
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Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm BMC Bioinform. (IF 3.0) Pub Date : 2024-03-12 Ying Liang, XingRui Yin, YangSen Zhang, You Guo, YingLong Wang
RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction
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DiseaseNet: a transfer learning approach to noncommunicable disease classification BMC Bioinform. (IF 3.0) Pub Date : 2024-03-11 Steven Gore, Bailey Meche, Danyang Shao, Benjamin Ginnett, Kelly Zhou, Rajeev K. Azad
As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships
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xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model BMC Bioinform. (IF 3.0) Pub Date : 2024-03-10 Thanh Hai Dang, Tien Anh Vu
Predicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need
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DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies BMC Bioinform. (IF 3.0) Pub Date : 2024-03-09 Chuanqi Lao, Pengfei Zheng, Hongyang Chen, Qiao Liu, Feng An, Zhao Li
The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods
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Bayesian inference for identifying tumour-specific cancer dependencies through integration of ex-vivo drug response assays and drug-protein profiling BMC Bioinform. (IF 3.0) Pub Date : 2024-03-08 Hanwen Xing, Christopher Yau
The identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound
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Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification BMC Bioinform. (IF 3.0) Pub Date : 2024-03-08 Onder Tutsoy, Gizem Gul Koç
Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet. This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature
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Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model BMC Bioinform. (IF 3.0) Pub Date : 2024-03-07 Shahid Akbar, Ali Raza, Quan Zou
Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat
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GPDminer: a tool for extracting named entities and analyzing relations in biological literature BMC Bioinform. (IF 3.0) Pub Date : 2024-03-06 Yeon-Ji Park, Geun-Je Yang, Chae-Bong Sohn, Soo Jun Park
The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume
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DVA: predicting the functional impact of single nucleotide missense variants BMC Bioinform. (IF 3.0) Pub Date : 2024-03-06 Dong Wang, Jie Li, Edwin Wang, Yadong Wang
In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional
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Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact BMC Bioinform. (IF 3.0) Pub Date : 2024-03-06 Xiaolu Xu, Zitong Qi, Lei Wang, Meiwei Zhang, Zhaohong Geng, Xiumei Han
Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. In the present work, we propose a method for identifying driver
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tRigon: an R package and Shiny App for integrative (path-)omics data analysis BMC Bioinform. (IF 3.0) Pub Date : 2024-03-05 David L. Hölscher, Michael Goedertier, Barbara M. Klinkhammer, Patrick Droste, Ivan G. Costa, Peter Boor, Roman D. Bülow
Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation
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Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions BMC Bioinform. (IF 3.0) Pub Date : 2024-03-05 Chengzhou Wu, Xichen Mou, Hongmei Zhang
DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and
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BSXplorer: analytical framework for exploratory analysis of BS-seq data BMC Bioinform. (IF 3.0) Pub Date : 2024-03-04 Konstantin Yuditskiy, Igor Bezdvornykh, Anastasiya Kazantseva, Alexander Kanapin, Anastasia Samsonova
Bisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale
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Ideal adaptive control in biological systems: an analysis of \(\mathbb {P}\)-invariance and dynamical compensation properties BMC Bioinform. (IF 3.0) Pub Date : 2024-03-04 Akram Ashyani, Yu-Heng Wu, Huan-Wei Hsu, Torbjörn E. M. Nordling
Dynamical compensation (DC) provides robustness to parameter fluctuations. As an example, DC enables control of the functional mass of endocrine or neuronal tissue essential for controlling blood glucose by insulin through a nonlinear feedback loop. Researchers have shown that DC is related to the structural unidentifiability and the $$\mathbb {P}$$ -invariance property. The $$\mathbb {P}$$ -invariance
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Analyzing postprandial metabolomics data using multiway models: a simulation study BMC Bioinform. (IF 3.0) Pub Date : 2024-03-04 Lu Li, Shi Yan, Barbara M. Bakker, Huub Hoefsloot, Bo Chawes, David Horner, Morten A. Rasmussen, Age K. Smilde, Evrim Acar
Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis
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Holomics - a user-friendly R shiny application for multi-omics data integration and analysis BMC Bioinform. (IF 3.0) Pub Date : 2024-03-04 Katharina Munk, Daria Ilina, Lisa Ziemba, Günter Brader, Eva M. Molin
An organism’s observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological
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Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification BMC Bioinform. (IF 3.0) Pub Date : 2024-03-01 Leandro Y. S. Okimoto, Rayol Mendonca-Neto, Fabíola G. Nakamura, Eduardo F. Nakamura, David Fenyö, Claudio T. Silva
In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature’s reliance on many genes presents
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AStruct: detection of allele-specific RNA secondary structure in structuromic probing data BMC Bioinform. (IF 3.0) Pub Date : 2024-03-01 Qingru Xu, Xiaoqiong Bao, Zhuobin Lin, Lin Tang, Li-na He, Jian Ren, Zhixiang Zuo, Kunhua Hu
Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing
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A clustering procedure for three-way RNA sequencing data using data transformations and matrix-variate Gaussian mixture models BMC Bioinform. (IF 3.0) Pub Date : 2024-03-01 Theresa Scharl, Bettina Grün
RNA sequencing of time-course experiments results in three-way count data where the dimensions are the genes, the time points and the biological units. Clustering RNA-seq data allows to extract groups of co-expressed genes over time. After standardisation, the normalised counts of individual genes across time points and biological units have similar properties as compositional data. We propose the
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CNCA aligns small annotated genomes BMC Bioinform. (IF 3.0) Pub Date : 2024-02-29 Jean-Noël Lorenzi, François Graner, Virginie Courtier-Orgogozo, Guillaume Achaz
To explore the evolutionary history of sequences, a sequence alignment is a first and necessary step, and its quality is crucial. In the context of the study of the proximal origins of SARS-CoV-2 coronavirus, we wanted to construct an alignment of genomes closely related to SARS-CoV-2 using both coding and non-coding sequences. To our knowledge, there is no tool that can be used to construct this type
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An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome BMC Bioinform. (IF 3.0) Pub Date : 2024-02-29 Hua Chai, Siyin Lin, Junqi Lin, Minfan He, Yuedong Yang, Yongzhong OuYang, Huiying Zhao
Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet
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HormoNet: a deep learning approach for hormone-drug interaction prediction BMC Bioinform. (IF 3.0) Pub Date : 2024-02-28 Neda Emami, Reza Ferdousi
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features
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kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R BMC Bioinform. (IF 3.0) Pub Date : 2024-02-28 Louis J. M. Aslett, Ryan R. Christ
Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as
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Protein embedding based alignment BMC Bioinform. (IF 3.0) Pub Date : 2024-02-28 Benjamin Giovanni Iovino, Yuzhen Ye
Despite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20–35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation in the 1970’s to generate alignments, however, these matrices do not work well to score alignments within the twilight zone. We
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GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM BMC Bioinform. (IF 3.0) Pub Date : 2024-02-27 K. M. Tahsin Hassan Rahit, Vladimir Avramovic, Jessica X. Chong, Maja Tarailo-Graovac
Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used
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Correction: Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data BMC Bioinform. (IF 3.0) Pub Date : 2024-02-23 Sedighe Rastaghi, Azadeh Saki, Hamed Tabesh
Correction: BMC Bioinformatics (2024) 25:57 https://doi.org/10.1186/s12859-024-05678-w Following publication of the original article [1], the authors identified an error in the author name of Sedighe Rastaghi. The incorrect author name is: Sedighe Rastahi. The correct author name is: Sedighe Rastaghi. The author group has been updated above and the original article [1] has been corrected. Rastaghi
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PRFect: a tool to predict programmed ribosomal frameshifts in prokaryotic and viral genomes BMC Bioinform. (IF 3.0) Pub Date : 2024-02-22 Katelyn McNair, Peter Salamon, Robert A. Edwards, Anca M. Segall
One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence and shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain. More importantly
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Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Il-Youp Kwak, Byeong-Chan Kim, Juhyun Lee, Taein Kang, Daniel J. Garry, Jianyi Zhang, Wuming Gong
The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like
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CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Philippe Hauchamps, Babak Bayat, Simon Delandre, Mehdi Hamrouni, Marie Toussaint, Stephane Temmerman, Dan Lin, Laurent Gatto
With the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses
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Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Shihui He, Lijun Yun, Haicheng Yi
Identification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs
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A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Haiyue Kuang, Zhen Zhang, Bin Zeng, Xin Liu, Hao Zuo, Xingye Xu, Lei Wang
In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations. In this manuscript
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Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Huanrong Tang, Yaowu Wang, Jianquan Ouyang, Jinlin Wang
Cryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and
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SNVstory: inferring genetic ancestry from genome sequencing data BMC Bioinform. (IF 3.0) Pub Date : 2024-02-20 Audrey E. Bollas, Andrei Rajkovic, Defne Ceyhan, Jeffrey B. Gaither, Elaine R. Mardis, Peter White
Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry
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Drug–target affinity prediction with extended graph learning-convolutional networks BMC Bioinform. (IF 3.0) Pub Date : 2024-02-16 Haiou Qi, Ting Yu, Wenwen Yu, Chenxi Liu
High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug–target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation
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Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology BMC Bioinform. (IF 3.0) Pub Date : 2024-02-16 Arnab Kole, Arup Kumar Bag, Anindya Jyoti Pal, Debashis De
Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring’s effectiveness in computational network biology, more precisely in analyzing protein–protein interaction (PPI) networks to gain insights about the viral infections and its consequences
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Fasta2Structure: a user-friendly tool for converting multiple aligned FASTA files to STRUCTURE format BMC Bioinform. (IF 3.0) Pub Date : 2024-02-15 Adam Bessa-Silva
The STRUCTURE software has gained popularity as a tool for population structure and genetic analysis. Nevertheless, formatting data to meet STRUCTURE's specific requirements can be daunting and susceptible to errors, especially when handling multilocus data. This article highlights the creation of a graphical user interface (GUI) application tailored to streamline the process of converting multiple
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Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data BMC Bioinform. (IF 3.0) Pub Date : 2024-02-14 Valentina Crippa, Emanuela Fina, Daniele Ramazzotti, Rocco Piazza
Copy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic
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Torch-eCpG: a fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units BMC Bioinform. (IF 3.0) Pub Date : 2024-02-14 Kord M. Kober, Liam Berger, Ritu Roy, Adam Olshen
Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition
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Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond BMC Bioinform. (IF 3.0) Pub Date : 2024-02-14 Anthony Baptista, Galadriel Brière, Anaïs Baudot
Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such
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Interpretable deep learning methods for multiview learning BMC Bioinform. (IF 3.0) Pub Date : 2024-02-14 Hengkang Wang, Han Lu, Ju Sun, Sandra E. Safo
Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views
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123VCF: an intuitive and efficient tool for filtering VCF files BMC Bioinform. (IF 3.0) Pub Date : 2024-02-14 Milad Eidi, Samaneh Abdolalizadeh, Soheila Moeini, Masoud Garshasbi, Javad Zahiri
The advent of Next-Generation Sequencing (NGS) has catalyzed a paradigm shift in medical genetics, enabling the identification of disease-associated variants. However, the vast quantum of data produced by NGS necessitates a robust and dependable mechanism for filtering irrelevant variants. Annotation-based variant filtering, a pivotal step in this process, demands a profound understanding of the case-specific
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Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves BMC Bioinform. (IF 3.0) Pub Date : 2024-02-12 Michael A. Reiter, Julia A. Vorholt
Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly
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A supervised learning method for classifying methylation disorders BMC Bioinform. (IF 3.0) Pub Date : 2024-02-12 Jesse R. Walsh, Guangchao Sun, Jagadheshwar Balan, Jayson Hardcastle, Jason Vollenweider, Calvin Jerde, Kandelaria Rumilla, Christy Koellner, Alaa Koleilat, Linda Hasadsri, Benjamin Kipp, Garrett Jenkinson, Eric Klee
DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal
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Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction BMC Bioinform. (IF 3.0) Pub Date : 2024-02-09 Yongwen Zhuang, Na Yeon Kim, Lars G. Fritsche, Bhramar Mukherjee, Seunggeun Lee
Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity
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vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis BMC Bioinform. (IF 3.0) Pub Date : 2024-02-08 Dharmesh D. Bhuva, Chin Wee Tan, Ning Liu, Holly J. Whitfield, Nicholas Papachristos, Samuel C. Lee, Malvika Kharbanda, Ahmed Mohamed, Melissa J. Davis
Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE
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Correction: Multiple imputation and direct estimation for qPCR data with non-detects BMC Bioinform. (IF 3.0) Pub Date : 2024-02-07 Valeriia Sherina, Helene R. McMurray, Winslow Powers, Harmut Land, Tanzy M. T. Love, Matthew N. McCall
Correction: BMC Bioinformatics (2020) 21:545 https://doi.org/10.1186/s12859-020-03807-9 Following the publication of the original article [1], the authors identified that Appendix D was missing in Additional file 1. The additional file has been updated. The original article [1] has been corrected. Sherina, et al. Multiple imputation and direct estimation for qPCR data with non-detects. BMC Bioinform
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Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base BMC Bioinform. (IF 3.0) Pub Date : 2024-02-07 Ghanshyam Verma, Dietrich Rebholz-Schuhmann, Michael G. Madden
Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients’
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MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads BMC Bioinform. (IF 3.0) Pub Date : 2024-02-07 Amira Sami, Sara El-Metwally, M. Z. Rashad
The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing
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MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry BMC Bioinform. (IF 3.0) Pub Date : 2024-02-06 Ruimin Wang, Hengxuan Jiang, Miaoshan Lu, Junjie Tong, Shaowei An, Jinyin Wang, Changbin Yu
As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart
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A comparison of embedding aggregation strategies in drug–target interaction prediction BMC Bioinform. (IF 3.0) Pub Date : 2024-02-06 Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman
The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer