Computational Methods For Identification Research Articles

BIT: Bayesian Identification of Transcriptional Regulators from Epigenomics-Based Query Region Sets.

Transcriptional regulators (TRs) are master controllers of gene expression and play a critical role in both normal tissue development and disease progression. However, existing computational methods for identification of TRs regulating specific biological processes have significant limitations, such as relying on distance on a linear chromosome or binding motifs that have low specificity. Many also use statistical tests in ways that lack interpretability and rigorous confidence measures. We introduce BIT, a novel Bayesian hierarchical model for in-silico TR identification. Leveraging a comprehensive library of TR ChIP-seq data, BIT offers a fully integrated Bayesian approach to assess genome-wide consistency between user-provided epigenomic profiling data and the TR binding library, enabling the identification of critical TRs while quantifying uncertainty. It avoids estimation and inference in a sequential manner or numerous isolated statistical tests, thereby enhancing accuracy and interpretability. BIT successfully identified critical TRs in perturbation experiments, functionally essential TRs in various cancer types, and cell-type-specific TRs within heterogeneous cell populations, offering deeper biological insights into transcriptional regulation.

A hierarchical GNN across semantic and topological domains for predicting circRNA-microRNA interactions

Identifying circRNA-microRNA interactions (CMI) is a significant biomedical issue in recent years. This problem provides insights into using circRNA as biomarkers, developing cancer therapies and producing cancer vaccines. Using computational methods for identification is a more time-efficient and cost-effective approach. In computational methods, using graphs to represent and explore the CMI is a mainstream approach. However, existing relevant methods do not achieve optimal results by utilizing both the semantic information extracted from sequences and the topological information extracted from graph structures. To address this issue, we propose HGLMALLM, a graph contrastive learning method that learns node representation crossing both the semantic domain generated via motif-aware pre-trained LLMs and the topological domain extracted from hierarchical graph structures. Our method effectively addresses the issue in existing Message Passing Neural Network (MPNN) method that edge components losing heterogeneity after multiple iterations. Moreover, this method utilizes the heterogeneity of graph which is extended from the traditional bipartite graph to heterogeneous through the semantic domain. Two commonly used datasets were partitioned based on the distribution of node degrees. Then, we benchmarked our method against existing methods. In the independent testing set evaluation, it achieved a 3 % and 1 % improvement on two datasets. Our method demonstrated the best stability in ten-fold cross-validation on the training set. A test conducted on the peripheral components reveals robust performance of our model. A dataset collected from real scenarios was used to demonstrate the strong predictive ability of our method for identifying unidentified CMI.

RDR100: A Robust Computational Method for Identification of Krüppel-like Factors

Background: Krüppel-like factors (KLFs) are a family of transcription factors containing zinc fingers that regulate various cellular processes. KLF proteins are associated with human diseases, such as cancer, cardiovascular diseases, and metabolic disorders. The KLF family consists of 18 members with diverse expression profiles across numerous tissues. Accurate identification and annotation of KLF proteins is crucial, given their involvement in important biological functions. Although experimental approaches can identify KLF proteins precisely, large-scale identification is complicated, slow, and expensive. Methods: In this study, we developed RDR100, a novel random forest (RF)-based framework for predicting KLF proteins based on their primary sequences. First, we identified the optimal encodings for ten different features using a recursive feature elimination approach, and then trained their respective model using five distinct machine learning (ML) classifiers. Results: The performance of all models was assessed using independent datasets, and RDR100 was selected as the final model based on its consistent performance in cross-validation and independent evaluation. Conclusion: Our results demonstrate that RDR100 is a robust predictor of KLF proteins. RDR100 web server is available at https://procarb.org/RDR100/.

Peptide immunotherapy for aeroallergens.

Background: Allergen specific immunotherapy (SIT) has been used for more than a century. Researchers have been working to improve efficacy and reduce the side effects. Objective: We have reviewed the literature about peptides immunotherapy for inhaled allergens. The mechanism of SIT is to induce regulatory T (Treg) cells and to reduce T helper (Th)2 cells to induce class switching from IgE to IgG and induce blocking antibodies to inhibit allergen binding of IgE. Methods: The relevant published literatures on the peptide SIT for aeroallergens have been searched on the medline. Results: Modification of allergens and routes of treatment has been performed. Among them, many researchers were interested in peptide immunotherapy. T-cell epitope peptide has no IgE epitope, that is able to bind IgE, but rather induces Treg and reduces Th2 cells, which was considered an ideal therapy. Results from cellular and animal model studies have been successful. However, in clinical studies, T-cell peptide immunotherapy has failed to show efficacy and caused side effects, because of the high effective rate of placebo and the development of IgE against T-cell epitope peptides. Currently, the modifications of IgE-allergen binding by blocking antibodies are considered for successful allergen immunotherapy. Conclusion: Newly developed hypoallergenic B cell epitope peptides and computational identification methods hold great potential to develop new peptide immunotherapies.

A Computational Method for Identification of Functional SNPs in Human Noncoding Genome Regions based on Multi-feature Mining

<p>Single Nucleotide Polymorphism (SNP) is the variant on a single nucleotide in the genome. Functional SNP, as one of the most important molecular markers in disease research, has been widely used in various research fields, such as tumor pathogenesis, disease diagnosis and treatment, prognostic evaluation, drug development, etc. The number of functional SNPs in noncoding genome regions is much more than that in coding regions, and their detection is more difficult. In this work, a multi-feature mining based computational method is proposed to predict the functional SNPs in human noncoding genomes. We first analyzed the sequence properties, evolutionary conservation properties and epigenetic modification signal properties of the sample SNPs. Statistical methods together with multiple annotation data from genomes and epigenetics were used to mine high-dimensional discriminative features subsequently. In particular, the allele-specific features were designed to distinguish the function of SNPs with close locations. The random forest method was used to conduct feature dimension reduction and classification. The 10-fold cross-validation result showed the Area Under the Receiver Operating Characteristic Curve (AUC) of our method improved by 16.9% and 43.4% over existing methods GWAVA and CADD, respectively, illustrating that the allele-specific based features can help to distinguish functional and netural SNPs with near locations.</p> <p> </p>

Detection and identification of cis-regulatory elements using change-point and classification algorithms

BackgroundTranscriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation.ResultsWe extend an alignment-based method, changept, and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite.ConclusionsThe analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.

Automatic identification of small molecules that promote cell conversion and reprogramming.

SummaryControlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although transcription factors are able to promote cell reprogramming and transdifferentiation, methods based on their upregulation often show low efficiency. Small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here, we present DECCODE, an unbiased computational method for identification of such molecules based on transcriptional data. DECCODE matches a large collection of drug-induced profiles for drug treatments against a large dataset of primary cell transcriptional profiles to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive validation in the context of human induced pluripotent stem cells shows that DECCODE is able to prioritize drugs and drug combinations enhancing cell reprogramming. We also provide predictions for cell conversion with single drugs and drug combinations for 145 different cell types.

Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery.

Structural alerts are a simple and easy way to identify toxic compounds being widely used in environmental toxicology research and drug discovery. With the emergence of big data techniques in recent years and their applications in chemistry and toxicology, computational approaches have become a promising method to identify structural alerts. In this Review, we describe the recent progress in computational methods for identification of structural alerts and their applications in toxicology. Two major computational approaches, namely frequency analysis and interpretable machine learning models, are reviewed. Recent studies have shown that both approaches are superior to expert systems with respect to predictive capability. Methodologies for defining the applicability domain of such approaches are also reviewed, with their importance stemming from their ability to not only improve the predictive performance of structural alert models but also ensure the confidence of a prediction. In addition to toxicity prediction, structural alerts could be also used to explain quantitative structure-activity relationship models and guide lead optimization in drug discovery. Nevertheless, there are still some challenges to be solved, such as how to address the co-existence of several structural alerts in one molecule, how to directly compare computationally derived structural alerts with expert systems, and how to explore new mechanisms of toxicity.

A semi-supervised machine learning framework for microRNA classification

BackgroundMicroRNAs (miRNAs) are a family of short, non-coding RNAs that have been linked to critical cellular activities, most notably regulation of gene expression. The identification of miRNA is a cross-disciplinary approach that requires both computational identification methods and wet-lab validation experiments, making it a resource-intensive procedure. While numerous machine learning methods have been developed to increase classification accuracy and thus reduce validation costs, most methods use supervised learning and thus require large labeled training data sets, often not feasible for less-sequenced species. On the other hand, there is now an abundance of unlabeled RNA sequence data due to the emergence of high-throughput wet-lab experimental procedures, such as next-generation sequencing.ResultsThis paper explores the application of semi-supervised machine learning for miRNA classification in order to maximize the utility of both labeled and unlabeled data. We here present the novel combination of two semi-supervised approaches: active learning and multi-view co-training. Results across six diverse species show that this multi-stage semi-supervised approach is able to improve classification performance using very small numbers of labeled instances, effectively leveraging the available unlabeled data.ConclusionsThe proposed semi-supervised miRNA classification pipeline holds the potential to identify novel miRNA with high recall and precision while requiring very small numbers of previously known miRNA. Such a method could be highly beneficial when studying miRNA in newly sequenced genomes of niche species with few known examples of miRNA.

Prediction of potential miRNA-disease associations using matrix decomposition and label propagation

Prediction of unobserved microRNA (miRNA)-disease associations is one of the most important research fields due to miRNA’s roles of diagnostic biomarkers and therapeutic targets for large number of human complex diseases. Thus, the development of effective computational methods for identification of novel miRNA-disease associations would provide a unique opportunity to design better therapeutic interventions. In this study, we presented a novel computational model named Matrix Decomposition and Label Propagation for MiRNA-Disease Association prediction (MDLPMDA) by integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. Based on the new adjacency matrix of miRNA-disease associations obtained from matrix decomposition through sparse learning method, the model is presented by implementing label propagation process on the constructed integrated miRNA similarity network and integrated disease similarity network, respectively, and then using an average ensemble strategy to combine the two different prediction models. At last, AUCs of 0.9222 and 0.8490 in global and local leave-one-out cross-validation (LOOCV) proved the model’s reliable performance. In addition, AUC of 0.9211+/-0.0004 in 5-fold cross-validation confirmed its accuracy and stability. We further implemented case studies to predict potential miRNAs associated with human complex diseases based on different versions of HMDD database. We also carried out case studies on diseases without any known related miRNAs to examine the prediction performance of MDLPMDA. At last, the analysis of the assessment results of cross validations and case studies indicated that MDLPMDA could be an effective method to infer novel miRNA-disease associations.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer.

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

Computational Methods for Identification and Modelling of Complex Biological Systems

3 pages,-- Editorial.-- This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Computational Methods for Identification of T Cell Neoepitopes in Tumors.

Cancer immunotherapy has experienced several major breakthroughs in the past decade. Most recently, technical advances in next-generation sequencing methods have enabled discovery of tumor-specific mutations leading to protective T cell neoepitopes. Many of the successes are enabled by computational methods, which facilitate processing of raw data, mapping of mutations, and prediction of neoepitopes. In this book chapter, we provide an overview of the computational tasks related to the identification of neoepitopes, propose specific tools and best practices, and discuss strengths, weaknesses, and future challenges.

Determination of the longitudinal compressive strength of a CFRP ply through a tensile test on a laminate

In this study, an innovative test is proposed to identify the longitudinal compressive strength of a unidirectional ply. The key idea consists in designing a laminate that, when subjected to a tensile loading, fails by compressive failure in its central 90°-ply, due to the Poisson effect, without any prior damage. Six specimens have been tensile tested to failure. No intra-laminar matrix damage could be detected before the final failure. Fibre kinking in the 90°-ply is observed experimentally in the failed specimens. This damage mechanism, located in the gauge section of the specimens, leads to the final failure. A fast computational identification method is used to determine the longitudinal compressive stress and strain within the 90°-ply at failure, from this specific tensile test. The identified average failure properties are consistent with those obtained through conventional compression tests, but the associated scattering is much lower. Consequently, this innovative method leads to an increase in the design allowable, resulting in higher performance designs.

Identification of miRNAs and target genes regulating catechin biosynthesis in tea (Camellia sinensis)

MicroRNAs (miRNAs) are endogenous non-protein-coding small RNAs that play crucial and versatile regulatory roles in plants. Using a computational identification method, we identified 55 conserved miRNAs in tea (Camellia sinensis) by aligning miRNA sequences of different plant species with the transcriptome library of tea strain 1005. We then used quantitative real-time PCR (qRT-PCR) to analyze the expression of 31 identified miRNAs in tea leaves of different ages, thereby verifying the existence of these miRNAs and confirming the reliability of the computational identification method. We predicted which miRNAs were involved in catechin synthesis using psRNAtarget Software based on conserved miRNAs and catechin synthesis pathway-related genes. Then, we used RNA ligase-mediated rapid amplification of cDNA ends (RLM-RACE) to obtain seven miRNAs cleaving eight catechin synthesis pathway-related genes including chalcone synthase (CHS), chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), anthocyanidin reductase (ANR), leucoanthocyanidin reductase (LAR), and flavanone 3-hydroxylase (F3H). An expression analysis of miRNAs and target genes revealed that miR529d and miR156g-3p were negatively correlated with their targets CHI and F3H, respectively. The expression of other miRNAs was not significantly related to their target genes in the catechin synthesis pathway. The RLM-RACE results suggest that catechin synthesis is regulated by miRNAs that can cleave genes involved in catechin synthesis.

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m5C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m5C sites in RNA precisely. The laboratory techniques and procedures are available to identify m5C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m5C sites from non- m5C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

An integrative model for the identification of key players of cancer networks

• A computational method for identification of cancer driver genes is proposed. • Non-differentiable convex optimization identifies relationships between genes. • Contributions of genomic data to cancer are formulated by convex maximization. • Gene expression, copy number, and gene interaction are used to analyze breast cancer. Uncovering miscoordination in a biological network is essential for the understanding of cellular malfunctions in cancer. Integrative analysis across multiple cellular levels may provide an opportunity to elucidate the miscoordination between the regulatory mechanisms in cancer cells. Here, we propose an integrative model for the identification of key players of the cancer-activated Multi-Type Interaction (MTI) gene network (KPOCN). To measure the functional associations between genes, using DNA copy number aberrations (CNAs) and gene expressions (GEs), we constructed three interacting weighted graphs: GEs affected by CNAs, CNAs by CNAs, and GEs by GEs. These three weighted graphs were mapped onto a single graph, in order to construct a MTI gene network by using their optimal combination. Finally, the effect of a single gene was determined by using the centrality and betweenness of node scores in the MTI network. We first tested KPOCN using simulated datasets, and afterward, we applied this model to the real breast cancer datasets. KPOCN was shown to identify successfully key regulators with their corresponding response variables (targets) when using the simulated data, and identified well-known breast cancer oncogenes. These results demonstrated that our model can be used for an efficient identification of key genes that affect cancer development. Source codes are available at http://gcancer.org/KPOCN.

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.

Design and Synthesis of a Pan-Janus Kinase Inhibitor Clinical Candidate (PF-06263276) Suitable for Inhaled and Topical Delivery for the Treatment of Inflammatory Diseases of the Lungs and Skin.

By use of a structure-based computational method for identification of structurally novel Janus kinase (JAK) inhibitors predicted to bind beyond the ATP binding site, a potent series of indazoles was identified as selective pan-JAK inhibitors with a type 1.5 binding mode. Optimization of the series for potency and increased duration of action commensurate with inhaled or topical delivery resulted in potent pan-JAK inhibitor 2 (PF-06263276), which was advanced into clinical studies.