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نتیجه جستجو - بیوانفورماتیک

تعداد مقالات یافته شده: 114
ردیف عنوان نوع
1 Graph Kernels Encoding Features of All Subgraphs by Quantum Superposition
ویژگی های رمزگزاری هسته های گراف زیرگراف ها با برهم نهی کوانتومی-2022
Graph kernels are often used in bioinformatics and network applications to measure the similarity between graphs; therefore, they may be used to construct efficient graph classifiers. Many graph kernels have been developed thus far, but to the best of our knowledge there is no existing graph kernel that uses some features explicitly extracted from all subgraphs to measure similarity. We propose a novel graph kernel that applies a quantum computer to measure the similarity obtained from all subgraphs by fully exploiting the power of quantum superposition to encode every subgraph into a feature of particular form. For the construction of the quantum kernel, we develop an efficient protocol that clears the index information of the subgraphs encoded in the quantum state. We also prove that the quantum computer requires less query complexity to construct the feature vector than the classical sampler used to approximate the same vector. A detailed numerical simulation of a bioinformatics problem is presented to demonstrate that, in many cases, the proposed quantum kernel achieves better classification accuracy than existing graph kernels.
Index Terms: Quantum computing | machine learning | bioinfomatics.
مقاله انگلیسی
2 Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیک‌های بینایی کامپیوتری برای طبقه‌بندی زنبورها و تقلیدهای زنبور عسل-2022
Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology
مقاله انگلیسی
3 Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance
استفاده از بیوانفورماتیک استاندارد برای تجزیه و تحلیل امضاهای DNA قارچی اعمال شده برای پیشروی نمونه-2020
The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.
Keywords: Forensic microbiology | Bioinformatics | Metabarcoding | Sample provenance
مقاله انگلیسی
4 Characterisation and computational analysis of a novel lipase nanobio-based reagent for visualising latent fingerprints on water-immersed glass slides
خصوصیات و تجزیه و تحلیل محاسباتی یک معرف مبتنی بر نانو بیو لیپاز برای تجسم اثر انگشت نهان در اسلایدهای شیشه غوطه ور در آب-2020
Considering the significant evidential values of fingerprints in underwater criminal investigations and the need to visualise them using a user- and environmentally-friendly reagent, development of a novel, rapid and relatively greener nanobio-based reagent (NBR) is deemed beneficial. Lipase from the commercial Candida rugosa immobilised onto acid-functionalised multi-walled carbon nanotubes (NBR) was used as the safer and cheap lipid-sensing reagent to visualise groomed whole/split fingerprints on non-porous objects immersed in stagnant tap water for up to 30 days under a laboratory-controlled setting. Attenuated Total Reflectance – Fourier Transform Spectrometry, Field Emission Scanning Electron Microscopy and bioinformatics (molecular docking and molecular dynamics simulations) were employed to characterise and confirm the attachment of NBR onto the lipid constituents of wet fingerprints. Chromatographic results further confirmed the presence of n-hexadecanoic and octadecanoic acids on fingerprints up to 30 days of immersion. Thus, NBR may potentially be useful as the future state-of-the-art fingerprint visualisation technology.
Keywords: Latent fingerprint | Nanobio-based reagent | Candida rugosa lipase | Bioinformatics | Forensic science
مقاله انگلیسی
5 Food allergomics based on high-throughput and bioinformatics technologies
آلرژیک مواد غذایی بر اساس فن آوری های توان بالا و بیوانفورماتیک-2020
Food allergy is a serious food safety problem worldwide, and the investigation of food allergens is the foundation of preventing and treating them, but relevant knowledge is far from sufficient. With the advent of the “big data era”, it has been possible to investigate food allergens by high-throughput methods, proposing the concept of allergomics. Allergomics is the discipline studying the repertoire of allergens, which has relatively higher throughput and is faster and more sensitive than conventional methods. This review introduces the basis of allergomics and summarizes its major strategies and applications. Particularly, strategies based on immunoblotting, phage display, allergen microarray, and bioinformatics are reviewed in detail, and the advantages and limitations of each strategy are discussed. Finally, further development of allergomics is predicted. This provides basic theories and recent advances in food allergomics research, which could be insightful for both food allergy research and practical applications.
Keywords: High-throughput | Bioinformatics | Allergome | Allergomics | Food allergy
مقاله انگلیسی
6 Artificial intelligence (AI) and big data in cancer and precision oncology
هوش مصنوعی و داده های بزرگ در سرطان و انکولوژی دقیق -2020
Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.
Keywords: Artificial intelligence | Machine learning | Deep learning | Big datasets | Precision oncology | NGS and bioinformatics | Medical imaging | Digital pathology | Diagnosis | Treatment | Prognosis and drug discovery
مقاله انگلیسی
7 Integrative systematic review meta-analysis and bioinformatics identifies MicroRNA-21 and its target genes as biomarkers for colorectal adenocarcinoma
متاآنالیز سیستماتیک یکپارچه و تجزیه و تحلیل بیوانفورماتیک شناسایی MicroRNA-21 و ژن های هدف آن به عنوان نشانگرهای تجاری برای آدنوکارسینوم روده بزرگ-2020
Background: Advanced colorectal has poor survival and are difficult to treat. Therefore, there is an urgent need for biomarkers to diagnose this cancer at earlier manageable stages. Micro-RNAs (miRNAs) are amongst the most significant biomarkers that have shown promise in improving management and early detection of different types of cancers. However, since MiRNAs are non-coding, the main limitation of using them as biomarkers is that they do not have associated phenotype and therefore difficult to validate using other techniques. This makes it difficult to understand the mechanism of miRNA is disease initiation and progression, therefore any methodology that can provide semantics to miRNA expression would enhance the understanding of the role of miRNA in disease. Methods: Here we report an integrative meta-analysis and bioinformatics methodology that showed microRNA- 21 and its associated target mRNA to be the most significant predictive biomarkers for colorectal adenoma and adenocarcinoma. After drawing key inferences by meta-analysis, the authors then developed a bioinformatics method to identify mir-21 gene targeting in a specific tissue using two different bioinformatics approaches; absolute GSEA (Gene Set Enrichment Analysis) and LIMMA (Linear Models for MicroArray data) to identify differentially expressed genes of miRNA-21. Results: Results from GSEA intersection with mir-21 gene targets was a subset of longer gene list that was obtained from the GEO2R intersect. In our study, both of longer GEO2R gene target list and the more focused GSEA list established the fact that mir-21 target numerous functional pathways that are mostly interconnected. Our three steps bioinformatics approach identified ABCB1, HPGD, BCL2, TIAM1, TLR3, and PDCD4 as common targets for mir-21 in both of adenoma as well as adenocarcinoma suggesting they are biomarkers for early CRC. Conclusions: The approach in this study proposed combining the big data from the scientific literature together with novel bioinformatics to bring about a methodology that can be used to first identify which microRNAs are involved in a specific disease, and then to identify a panel of biomarkers derived from the microRNAs target genes, and from these target genes the functional significance of these microRNAs can be inferred providing better clinical value for the surgeon
Keywords: Tissue/serum microRNA-21 | Biomarkers | Colorectal cancer | Bioinformatics
مقاله انگلیسی
8 Comprehensive Comparison of Cloud-Based NGS Data Analysis and Alignment Tools
مقایسه جامع ابزارهای تحلیل و تراز داده NGS مبتنی بر ابر-2020
Next-Generation Sequencing (NGS) is very helpful for conducting DeoxyriboNucleic Acid (DNA) Sequencing. DNA sequencing is the process for determining the order (sequence) of the main chemical bases in the DNA. Analyzing human DNA sequencing is important for determining the possibility that a person will develop certain diseases, and/or the ability to respond to medication. However, the NGS process is a complicated and resource-hungry technical process. To solve this dilemma, the majority of NGS software systems are deployed as cloud-based services distributed over cloud-based platforms. Cloud-based platforms provide promising solutions for the computationally intensive tasks required by the NGS data analysis. This work provides a comprehensive investigation of cloud-based NGS data analysis and alignment tools, both the commercial and the open-source tools. We also discuss in detail the main features and setup requirements for each tool, and then compare and contrast between them. Moreover, we extensively analyze and classify the studied NGS data analysis and alignment tools to help NGS biomedical researchers and clinicians in finding appropriate tools for their work, while understanding the similarities and the differences between them.
Keywords: Next-Generation Sequencing (NGS) | Sequence Alignment | Cloud Computing | Big Data | Bioinformatics
مقاله انگلیسی
9 Mining featured biomarkers associated with vascular invasion in HCC by bioinformatics analysis with TCGA RNA sequencing data
استخراج نشانگرهای زیستی مرتبط با تهاجم عروق در HCC با تجزیه و تحلیل بیوانفورماتیک با داده های توالی TCNA RNA-2019
This study aims to identify the feature genes associated with vascular invasion in hepatocellular carcinoma (HCC). Here, the RNA sequencing data related to vascular invasion in The Cancer Genome Atlas (TCGA) database, including 292 HCC patients with complete clinical data were included in our study as the training dataset for construction and E-TABM-36, including 41 HCC patients with complete clinical data was used as the validation dataset. Following data normalization, differentially expressed mRNA and copy number (CN) were selected between with and without vascular invasion samples. A support vector machine (SVM) classifier was constructed and validated in GSE9828 and GSE20017 datasets. Total 59 feature genes were found by the SVM classifier. Using Cox regression analysis, three clinical features, including Patholigic T, Stage and vascular invasion and 6 optimal prognostic genes, including ANO1, EPHX2, GFRA1, OLFM2, SERPINA10 and TKT were significantly correlated with prognosis. A risk score formula was developed to assess the prognostic value of 6 optimal prognostic genes, which were identified to possess the most remarkable correlation with overall survival in HCC patients. By performing in vitro experiments, we observed TKT was significantly increased, but OLFM2 was decreased in high metastatic potential HCC cell lines (SK-HEP-1 and MHCC-97 H) compared with low metastatic potential cell line Huh7 and normal human liver cell line LO2 using western blotting analysis. Knockdown of TKT in MHCC-97H or overexpression of OLFM2 in SK-HEP-1 significantly suppressed cell migration and invasion using transwell assays. Our results demonstrated that TKT and OLFM2 might be novel independent biomarkers for predicting survival based on the presence of vascular invasion in patients with HCC.
Keywords: Hepatocellular carcinoma | Vascular invasion | Support vector machine | Prognosis
مقاله انگلیسی
10 Microarray-based data mining reveals key genes and potential therapeutic drugs for Cadmium-induced prostate cell malignant transformation
داده کاوی مبتنی بر ریزآرایه ژنهای کلیدی و داروهای درمانی بالقوه را برای تحول بدخیم سلولهای پروستات ناشی از کادمیوم-2019
Increasing evidence showed that Cadmium (Cd) can accumulate in the body and damage cells, resulting in cancerigenesis of the prostate with complex mechanisms. In the present study, we aimed to explore the possible key genes, pathways and therapeutic drugs using bioinformatics methods. Microarray-based data were retrieved and analyzed to screen differentially expressed genes (DEGs) between Cd-treated prostate cells and controls. Then, functions of the DEGs were annotated and hub genes were screened. Next, key genes were selected from the hub genes via validation in a prostate cancer cohort from The Cancer Genome Atlas (TCGA). Afterward, potential drugs were further predicted. Consequently, a gene expression profile, GSE9951, was retrieved. Then, 361 up-regulated and 30 down-regulated DEGs were screened out, which were enriched in various pathways. Among the DEGs, seven hub genes (HSPA5, HSP90AB1, RHOA, HSPD1, MAD2L1, SKP2, and CCT2) were dysregulated in prostate cancer compared to normal controls, and two of them (HSPD1 and CCT2) might influence the prostate cancer prognosis. Lastly, ionomycin was predicted to be a potential agent reversing Cd-induced prostate cell malignant transformation. In summary, the present study provided novel evidence regarding the mechanisms of Cd-induced prostate cell malignant transformation, and identified ionomycin as a potential small molecule against Cd toxicity.
Keywords: Cadmium | Differentially expressed genes | Prostate carcinoma | TCGA | Bioinformatics
مقاله انگلیسی
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