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Targeted Metabolomics Analyses for Brain Tumor Margin Assessment During Surgery

Bioinformatics Oxford Journals - Thu, 05/05/2022 - 5:30am
AbstractMotivationIdentification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e., using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance.ResultsIn this study, we show that identifying informative regions in the HRMAS NMR spectrum and using them for tumor margin assessment improves the prediction power. We use the spectra normalized with the ERETIC (electronic reference to access in vivo concentrations) method which uses an external reference signal to calibrate the HRMAS NMR spectrum. We train models to predict quantities of metabolites from annotated regions of this spectrum. Using these predictions for tumor margin assessment provides performance improvements up to 4.6% AUC-ROC and 2.8% AUC-PR. We validate the importance of various tumor biomarkers and identify a novel region between 7.97 ppm and 8.09 ppm as a new candidate for a glioma biomarker.AvailabilityThe code is released at https://github.com/ciceklab/targeted_brain_tumor_margin_assessment. The data used in this study is released at https://zenodo.org/record/5774947.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Predicting circRNA-drug sensitivity associations via graph attention auto-encoder

BMC Bioinformatics - Wed, 04/05/2022 - 5:30am
Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the ...
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KnotAli: informed energy minimization through the use of evolutionary information

BMC Bioinformatics - Tue, 03/05/2022 - 5:30am
Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a fa...
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Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding

BMC Bioinformatics - Mon, 02/05/2022 - 5:30am
Drug discovery is time-consuming and costly. Machine learning, especially deep learning, shows great potential in quantitative structure–activity relationship (QSAR) modeling to accelerate drug discovery proce...
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Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data

BMC Bioinformatics - Mon, 02/05/2022 - 5:30am
Although single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous hom...
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GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

BMC Bioinformatics - Mon, 02/05/2022 - 5:30am
Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq ex...
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tReasure: R-based GUI package analyzing tRNA expression profiles from small RNA sequencing data

BMC Bioinformatics - Mon, 02/05/2022 - 5:30am
Recent deep sequencing technologies have proven to be valuable resources to gain insights into the expression profiles of diverse tRNAs. However, despite these technologies, the association of tRNAs with diver...
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Small Molecule Generation via Disentangled Representation Learning

Bioinformatics Oxford Journals - Mon, 02/05/2022 - 5:30am
AbstractMotivationExpanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance cheminformatics, drug discovery, biotechnology, and material science. In-silico molecular design remains challenging, primarily due to the complexity of the chemical space and the non-trivial relationship between chemical structures and biological properties. Deep generative models that learn directly from data are intriguing, but they have yet to demonstrate interpretability in the learned representation, so we can learn more about the relationship between the chemical and biological space. In this paper, we advance research on disentangled representation learning for small molecule generation. We build on recent work by us and others on deep graph generative frameworks, which capture atomic interactions via a graph-based representation of a small molecule. The methodological novelty is how we leverage the concept of disentanglement in the graph variational autoencoder framework both to generate biologically-relevant small molecules and to enhance model interpretability.ResultsExtensive qualitative and quantitative experimental evaluation in comparison with state of the art models demonstrate the superiority of our disentanglement framework. We believe this work is an important step to address key challenges in small molecule generation with deep generative frameworks.AvailabilityTraining and generated data are made available at https://ieee-dataport.org/documents/dataset-disentangled-representation-learning-interpretable-molecule-generation. All code is made available at https://anonymous.4open.science/r/D-MolVAE-2799/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
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Enhancer transcription detected in the nascent transcriptomic landscape of bread wheat

Genome Biology - BiomedCentral - Sat, 30/04/2022 - 5:30am
The precise spatiotemporal gene expression is orchestrated by enhancers that lack general sequence features and thus are difficult to be computationally identified. By nascent RNA sequencing combined with epig...
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GEInfo: an R package for gene-environment interaction analysis incorporating prior information

Bioinformatics Oxford Journals - Fri, 29/04/2022 - 5:30am
AbstractSummaryGene-environment (G-E) interactions have important implications for many complex diseases. With higher dimensionality and weaker signals, G-E interaction analysis is more challenged than the analysis of main G (and E) effects. The accumulation of published literature makes it possible to borrow strength from prior information and improve analysis. In a recent study, a “quasi-likelihood + penalization” approach was developed to effectively incorporate prior information. Here, we first extend it to linear, logistic, and Poisson regressions. Such models are much more popular in practice. More importantly, we develop the R package GEInfo, which realizes this approach in a user-friendly manner. To facilitate direct comparison and routine data analysis, the package also includes functions for alternative methods and visualization.AvailabilityThe package is available at https://CRAN.R-project.org/package=GEInfo.Supplementary informationSupplementary materialsSupplementary materials are available at Bioinformatics online.
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ATHENA: Analysis of Tumor Heterogeneity from Spatial Omics Measurements

Bioinformatics Oxford Journals - Fri, 29/04/2022 - 5:30am
AbstractSummaryTumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially-aware manner are largely missing. We present ATHENA, a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA employs graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems.Availability and ImplementationATHENA is available as a Python package under an open-source licence at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
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Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization

Bioinformatics Oxford Journals - Fri, 29/04/2022 - 5:30am
AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) technologies have been testified revolutionary for their promotion on the profiling of single-cell transcriptomes at single-cell resolution. Excess zeros due to various technical noises, called dropouts, will mislead downstream analyses. Therefore, it is crucial to have accurate imputation methods to address the dropout problem.ResultsIn this paper, we develop a new dropout imputation method for scRNA-seq data based on multi-objective optimization. Our method is different from existing ones, which assume that the underlying data has a preconceived structure and impute the dropouts according to the information learned from such structure. We assume that the data combines three types of latent structures, including the horizontal structure (genes are similar to each other), the vertical structure (cells are similar to each other), and the low-rank structure. The combination weights and latent structures are learned using multi-objective optimization. And, the weighted average of the observed data and the imputation results learned from the three types of structures are considered as the final result. Comprehensive downstream experiments show the superiority of our method in terms of recovery of true gene expression profiles, differential expression analysis, cell clustering and cell trajectory inference.AvailabilityThe R package is available at https://github.com/Zhangxf-ccnu/scMOO and https://zenodo.org/record/5785195. The codes to reproduce the downstream analyses in this paper can be found at https://github.com/Zhangxf-ccnu/scMOO_experiments_codes and https://zenodo.org/record/5786211.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
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CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain

Bioinformatics Oxford Journals - Fri, 29/04/2022 - 5:30am
AbstractMotivationThe field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perform sub-optimal in non-standard domains like the clinical domain where a large gap between pre-training documents and target documents is observed. In this paper, we aim at closing this gap with domain-specific training of the language model and we investigate its effect on a diverse set of downstream tasks and settings.ResultsWe introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models by a large margin for ten clinical concept extraction tasks from two languages. In addition, we demonstrate how the transformer model can be further improved with our proposed task- and language-agnostic model architecture based on ensembles over random splits and cross-sentence context. Our studies in low-resource and transfer settings reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models, such as CLIN-X, for concept extraction in non-standard domains, but also show that our task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required.AvailabilityThe CLIN-X language models and source code for fine-tuning and transferring the model are publicly available at https://github.com/boschresearch/clin_x/ and the huggingface model hub.
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Correction to: MetaSquare: an integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification

Bioinformatics Oxford Journals - Fri, 29/04/2022 - 5:30am
Ministry of Science and Technology, Taiwan10.13039/501100004663AS-GCS-109-07
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August 2022