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Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data

BMC Bioinformatics - Wed, 22/09/2021 - 5:30am
With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising a...
Categories: Bioinformatics Trends

Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion

BMC Bioinformatics - Wed, 22/09/2021 - 5:30am
Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert path...
Categories: Bioinformatics Trends

Predicting chemotherapy response using a variational autoencoder approach

BMC Bioinformatics - Wed, 22/09/2021 - 5:30am
Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are pu...
Categories: Bioinformatics Trends

Revisiting genetic artifacts on DNA methylation microarrays exposes novel biological implications

Genome Biology - BiomedCentral - Tue, 21/09/2021 - 5:30am
Illumina DNA methylation microarrays enable epigenome-wide analysis vastly used for the discovery of novel DNA methylation variation in health and disease. However, the microarrays’ probe design cannot fully c...
Categories: Bioinformatics Trends

Chromatin accessibility and regulatory vocabulary across indicine cattle tissues

Genome Biology - BiomedCentral - Tue, 21/09/2021 - 5:30am
Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions a...
Categories: Bioinformatics Trends

DLAB—Deep learning methods for structure-based virtual screening of antibodies

Bioinformatics Oxford Journals - Tue, 21/09/2021 - 5:30am
AbstractMotivationAntibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in-vivo and in-vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.ResultsWe demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.AvailabilityThe DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ATPdock: a template-based method for ATP-specific protein-ligand docking

Bioinformatics Oxford Journals - Tue, 21/09/2021 - 5:30am
AbstractMotivationAccurately identifying protein-ATP binding poses is significantly valuable for both basic structure biology and drug discovery. Although many docking methods have been designed, most of them require a user-defined binding site and are difficult to achieve a high-quality protein-ATP docking result. It is critical to develop a protein-ATP-specific blind docking method without user-defined binding sites.ResultsHere, we present ATPdock, a template-based method for docking ATP into protein. For each query protein, if no pocket site is given, ATPdock first identifies its most potential pocket using ATPbind, an ATP-binding site predictor; then, the template pocket, which is most similar to the given or identified pocket, is searched from the database of pocket-ligand structures using APoc, a pocket structural alignment tool; thirdly, the rough docking pose of ATP (rdATP) is generated using LS-align, a ligand structural alignment tool, to align the initial ATP pose to the template ligand corresponding to template pocket; finally, the Metropolis Monte Carlo simulation is used to fine-tune the rdATP under the guidance of AutoDock Vina energy function. Benchmark tests show that ATPdock significantly outperforms other state-of-the-art methods in docking accuracy.Availabilityhttps://jun-csbio.github.io/atpdock/Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Concentration optimization of combinatorial drugs using Markov chain-based models

BMC Bioinformatics - Tue, 21/09/2021 - 5:30am
Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently ...
Categories: Bioinformatics Trends

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

BMC Bioinformatics - Tue, 21/09/2021 - 5:30am
The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic ...
Categories: Bioinformatics Trends

LightGBM: accelerated genomically designed crop breeding through ensemble learning

Genome Biology - BiomedCentral - Mon, 20/09/2021 - 5:30am
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize l...
Categories: Bioinformatics Trends

STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions

Genome Biology - BiomedCentral - Mon, 20/09/2021 - 5:30am
Sequence Read Archive submissions to the National Center for Biotechnology Information often lack useful metadata, which limits the utility of these submissions. We describe the Sequence Taxonomic Analysis Too...
Categories: Bioinformatics Trends

ProFitFun: A Protein Tertiary Structure Fitness Function for Quantifying the Accuracies of Model Structures

Bioinformatics Oxford Journals - Mon, 20/09/2021 - 5:30am
AbstractMotivationAn accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are employed to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins.ResultsThe approach was extensively validated on a large test dataset (n = 25,005) of protein structures, comprising 23,661 models of 82 non-homologous proteins and 1,344 non-homologous experimental structures. Additionally, an external dataset of 40,000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further employed for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state of the art methods in terms of Spearman’s and Pearson’s correlation coefficients, average GDT-TS loss, sum of z-scores, and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design.Availabilityhttp://github.com/KYZ-LSB/ProTerS-FitFunSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

BMC Bioinformatics - Mon, 20/09/2021 - 5:30am
This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph.
Categories: Bioinformatics Trends

Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

BMC Bioinformatics - Mon, 20/09/2021 - 5:30am
The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between ge...
Categories: Bioinformatics Trends

ncDLRES: a novel method for non-coding RNAs family prediction based on dynamic LSTM and ResNet

BMC Bioinformatics - Mon, 20/09/2021 - 5:30am
Studies have proven that the same family of non-coding RNAs (ncRNAs) have similar functions, so predicting the ncRNAs family is helpful to the research of ncRNAs functions. The existing calculation methods ma...
Categories: Bioinformatics Trends

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases

BMC Bioinformatics - Sat, 18/09/2021 - 5:30am
Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30%...
Categories: Bioinformatics Trends

Fast and exact quantification of motif occurrences in biological sequences

BMC Bioinformatics - Sat, 18/09/2021 - 5:30am
Identification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estima...
Categories: Bioinformatics Trends

DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction

BMC Bioinformatics - Sat, 18/09/2021 - 5:30am
The study of gene essentiality is fundamental to understand the basic principles of life, as well as for applications in many fields. In recent decades, dozens of sets of essential genes have been determined u...
Categories: Bioinformatics Trends

Boolean factor graph model for biological systems: the yeast cell-cycle network

BMC Bioinformatics - Fri, 17/09/2021 - 5:30am
The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modelin...
Categories: Bioinformatics Trends

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