biological knowledge
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2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Mehrdad Mansouri ◽  
Sahand Khakabimamaghani ◽  
Leonid Chindelevitch ◽  
Martin Ester

Abstract Background There has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others. Methods To fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes. Results Evaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle’s predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations.


2022 ◽  
Vol 8 (1) ◽  
pp. 265-270
Author(s):  
L. Timoshina

Biological knowledge is one of the fundamental components of a common human culture, because without knowledge of biology it is unthinkable to develop an ecological way of thinking, to ensure the perception of the scientific principles of interaction of the Man-Nature system. The formation of a healthy lifestyle, the preservation of ecosystems, and the development of mankind are based on knowledge of biology. The purpose of the article is to develop and use a model of a methodology for organizing project-based education in biology in non-core institutions of secondary vocational education. The interrelation of theoretical knowledge and practical skills is a prerequisite for increasing the level of training of future specialists in any industry. In connection with the introduction of new FSES SPE into the work of educational institutions, secondary schools are required to improve the organization of the educational process and teaching materials. The involvement of students in the implementation of various projects leads to the development and development of project competence, and its level is determined by the number and quality of developed projects. The author concludes that the formation of competencies among students at secondary schools through project activities takes place.


Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 310
Author(s):  
Marta Sant ◽  
Adrià Bernat-Peguera ◽  
Eudald Felip ◽  
Mireia Margelí

Breast cancer is currently classified by immunohistochemistry. However, technological advances in the detection of circulating tumor DNA (ctDNA) have made new options available for diagnosis, classification, biological knowledge, and treatment selection. Breast cancer is a heterogeneous disease and ctDNA can accurately reflect this heterogeneity, allowing us to detect, monitor, and understand the evolution of the disease. Breast cancer patients have higher levels of circulating DNA than healthy subjects, and ctDNA can be used for different objectives at different timepoints of the disease, ranging from screening and early detection to monitoring for resistance mutations in advanced disease. In early breast cancer, ctDNA clearance has been associated with higher rates of complete pathological response after neoadjuvant treatment and with fewer recurrences after radical treatments. In metastatic disease, ctDNA can help select the optimal sequencing of treatments. In the future, thanks to new bioinformatics tools, the use of ctDNA in breast cancer will become more frequent, enhancing our knowledge of the biology of tumors. Moreover, deep learning algorithms may also be able to predict breast cancer evolution or treatment sensitivity. In the coming years, continued research and the improvement of liquid biopsy techniques will be key to the implementation of ctDNA analysis in routine clinical practice.


2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Alessandro Muscolino ◽  
Antonio Di Maria ◽  
Rosaria Valentina Rapicavoli ◽  
Salvatore Alaimo ◽  
Lorenzo Bellomo ◽  
...  

Abstract Background The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.


2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Aparna Elangovan ◽  
Yuan Li ◽  
Douglas E. V. Pires ◽  
Melissa J. Davis ◽  
Karin Verspoor

Abstract Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. Method We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. Results and conclusion The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter $$\approx 5700$$ ≈ 5700 (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.


2022 ◽  
Author(s):  
Tinna Reynisdottir ◽  
Kimberley Anderson ◽  
Leandros Boukas ◽  
Hans Bjornsson

Wiedemann-Steiner syndrome (WSS) is a neurodevelopmental disorder caused by de novo variants in KMT2A, which encodes a multi–domain histone methyltransferase. To gain insight into the currently unknown pathogenesis of WSS, we examined the spatial distribution of likely WSS–causing variants across the 15 different domains of KMT2A. Compared to variants in healthy controls, WSS variants exhibit a 64.1–fold overrepresentation within the CXXC domain – which mediates binding to unmethylated CpGs – suggesting a major role for this domain in mediating the phenotype. In contrast, we find no significant overrepresentation within the catalytic SET domain. Corroborating these results, we find that hippocampal neurons from Kmt2a–deficient mice demonstrate disrupted H3K4me1 preferentially at CpG-rich regions, but this has no systematic impact on gene expression. Motivated by these results, we combine accurate prediction of the CXXC domain structure by AlphaFold2 with prior biological knowledge to develop a classification scheme for missense variants in the CXXC domain. Our classifier achieved 96.0% positive and 92.3% negative predictive value on a hold–out test set. This classification performance enabled us to subsequently perform an in silico saturation mutagenesis and classify a total of 445 variants according to their functional effects. Our results yield a novel insight into the mechanistic basis of WSS and provide an example of how AlphaFold2 can contribute to the in silico characterization of variant effects with very high accuracy, establishing a paradigm potentially applicable to many other Mendelian disorders.


2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Pelin Gundogdu ◽  
Carlos Loucera ◽  
Inmaculada Alamo-Alvarez ◽  
Joaquin Dopazo ◽  
Isabel Nepomuceno

Abstract Background Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data. Results In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets. Conclusions Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.


2021 ◽  
Author(s):  
Ronghui You ◽  
Wei Qu ◽  
Hiroshi Mamitsuka ◽  
Shanfeng Zhu

Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring the biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with binding interaction convolution layer (BICL), which allows integrating all potential binding cores (in a given peptide) and the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as five-fold cross-validation, leave one molecule out, validation with independent testing sets, and binding core prediction. All these results with visualization of the predicted binding cores indicate the effectiveness and importance of properly modeling biological facts in deep learning for high performance and knowledge discovery. DeepMHCII is publicly available at https://weilab.sjtu.edu.cn/DeepMHCII/.


2021 ◽  
Vol 12 ◽  
Author(s):  
Valentina Azzollini ◽  
Stefania Dalise ◽  
Carmelo Chisari

Long-term disability caused by stroke is largely due to an impairment of motor function. The functional consequences after stroke are caused by central nervous system adaptations and modifications, but also by the peripheral skeletal muscle changes. The nervous and muscular systems work together and are strictly dependent in their structure and function, through afferent and efferent communication pathways with a reciprocal “modulation.” Knowing how altered interaction between these two important systems can modify the intrinsic properties of muscle tissue is essential in finding the best rehabilitative therapeutic approach. Traditionally, the rehabilitation effort has been oriented toward the treatment of the central nervous system damage with a central approach, overlooking the muscle tissue. However, to ensure greater effectiveness of treatments, it should not be forgotten that muscle can also be a target in the rehabilitation process. The purpose of this review is to summarize the current knowledge about the skeletal muscle changes, directly or indirectly induced by stroke, focusing on the changes induced by the treatments most applied in stroke rehabilitation. The results of this review highlight changes in several muscular features, suggesting specific treatments based on biological knowledge; on the other hand, in standard rehabilitative practice, a realist muscle function evaluation is rarely carried out. We provide some recommendations to improve a comprehensive muscle investigation, a specific rehabilitation approach, and to draw research protocol to solve the remaining conflicting data. Even if a complete multilevel muscular evaluation requires a great effort by a multidisciplinary team to optimize motor recovery after stroke.


Genetics ◽  
2021 ◽  
Author(s):  
Midori A Harris ◽  
Kim M Rutherford ◽  
Jacqueline Hayles ◽  
Antonia Lock ◽  
Jürg Bähler ◽  
...  

Abstract PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.


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