Information theory is employed in our unsupervised method, wherein parameters are automatically estimated, to determine the optimal statistical model complexity, thus circumventing the pitfalls of underfitting and overfitting, a common issue in model selection. Our models are designed for a wide variety of downstream studies—ranging from experimental structure refinement and de novo protein design to protein structure prediction—and are computationally inexpensive to sample from. Our mixture models are grouped under the name PhiSiCal(al).
PhiSiCal mixture models and programs enabling sampling are obtainable for download at http//lcb.infotech.monash.edu.au/phisical.
PhiSiCal mixture models and their associated sampling programs are available for download at http//lcb.infotech.monash.edu.au/phisical.
RNA design, essentially the inverse problem of RNA folding, involves the pursuit of a sequence or a set of sequences that are destined to adopt a predetermined structural form. Nonetheless, the sequences generated by existing algorithms frequently demonstrate a lack of ensemble stability, a deficiency that intensifies as sequence length increases. Particularly, only a small selection of sequences are discovered by each iteration of many methods, fulfilling the MFE requirement. These disadvantages narrow the scope of their practical application.
We present SAMFEO, an innovative optimization paradigm that iteratively optimizes ensemble objectives (equilibrium probability or ensemble defect), producing a substantial output of successfully designed RNA sequences. Our search technique strategically incorporates structural and ensemble level data across the optimization pipeline, including initialization, sampling, mutation, and update. Our algorithm, while displaying less complexity compared to others, uniquely designs thousands of RNA sequences addressing the Eterna100 benchmark puzzles. In addition, our algorithm exhibits the capacity to solve the greatest number of Eterna100 puzzles compared to every other general optimization-based technique within our analysis. Only baselines leveraging handcrafted heuristics tailored to a specific folding model achieve higher puzzle-solving performance than our work. Remarkably, our method outperforms in creating long sequences for structures modeled after the 16S Ribosomal RNA database.
This article's source code and accompanying data are located at https://github.com/shanry/SAMFEO.
This article's source code and accompanying data are available at this link: https//github.com/shanry/SAMFEO.
The genomic community is still confronted with the challenge of accurately predicting the regulatory function of non-coding DNA sequences based solely on their sequence. The integration of improved optimization algorithms, rapid GPU processing, and elaborate machine learning libraries allows for the creation and implementation of hybrid convolutional and recurrent neural network architectures to extract critical data points from non-coding DNA.
A comparative analysis of diverse deep learning architectures resulted in ChromDL, a neural network composed of bidirectional gated recurrent units, convolutional neural networks, and bidirectional long short-term memory units. This new architecture provides substantial improvements in predictive metrics for transcription factor binding sites, histone modifications, and DNase-I hyper-sensitive sites when compared to previous approaches. For precise classification of gene regulatory elements, a secondary model is essential. In comparison to previously established techniques, the model can also identify subtle transcription factor binding, thereby holding the potential to clarify the specificities of transcription factor binding motifs.
https://github.com/chrishil1/ChromDL contains the source code for the project ChromDL.
The repository https://github.com/chrishil1/ChromDL houses the ChromDL source code.
High-throughput omics data's growing abundance enables the consideration of personalized medicine focused on individual patients. Deep learning machine-learning models, applied to high-throughput data, significantly improve diagnostic outcomes in the context of precision medicine. The high-dimensional and small-sample nature of omics data necessitates deep learning models with a large number of parameters, making it essential to train with a limited data set. Moreover, the intricate interplay of molecular entities within an omics profile is consistent for all patients, not specific to a given individual.
AttOmics, a newly developed deep learning architecture using the self-attention mechanism, is detailed in this article. Initially, we segment each omics profile into clusters, each cluster comprising interconnected characteristics. The self-attention mechanism, when applied to the clusters of data, allows us to identify the unique interactions characteristic of a patient. Different experiments undertaken in this article illustrate that our model accurately predicts a patient's phenotype, requiring fewer parameters than are necessary for deep neural networks. Attention maps offer fresh perspectives on the crucial groupings associated with a given phenotype.
At https//forge.ibisc.univ-evry.fr/abeaude/AttOmics, users can obtain the AttOmics code and data. The Genomic Data Commons Data Portal provides access to TCGA data.
At https://forge.ibisc.univ-evry.fr/abeaude/AttOmics, one can find the AttOmics code and data; the Genomic Data Commons Data Portal facilitates access to TCGA data downloads.
The availability of transcriptomics data has expanded significantly due to improvements in sequencing methods, making them both high-throughput and less costly. Yet, the constrained supply of data obstructs the complete utilization of deep learning models' forecasting ability concerning phenotypes. A regularization strategy using artificial enhancement of the training sets, specifically data augmentation, is put forward. Data augmentation involves applying transformations to the training set that do not affect the labels. Syntax parsing of text data and geometric transformations applied to images are integral components of advanced data manipulation. Unfortunately, the transcriptomic world shows no record of these transformations. Consequently, generative adversarial networks (GANs), a type of deep generative model, have been put forward to create supplementary examples. In this article, we delve into GAN-based data augmentation methods, considering their impact on performance indicators and cancer phenotype classification.
Significant improvements in binary and multiclass classification performance are reported in this work, resulting from the implementation of augmentation strategies. Classifier training using 50 RNA-seq samples, unaided by augmentation, yields 94% accuracy for binary and 70% for tissue classification. hepatoma upregulated protein The addition of 1000 augmented samples yielded a remarkable 98% and 94% accuracy. The more elaborate architectures and the higher cost of GAN training procedures generate better results in data augmentation and improved quality of the generated data. Subsequent analysis of the generated data underscores the requirement for a comprehensive set of performance indicators to properly gauge its quality.
The publicly accessible data employed in this investigation originates from The Cancer Genome Atlas. The source code, ensuring reproducibility, is hosted in the GitLab repository https//forge.ibisc.univ-evry.fr/alacan/GANs-for-transcriptomics.
Publicly accessible data from The Cancer Genome Atlas is used in this research. On the GitLab repository https//forge.ibisc.univ-evry.fr/alacan/GANs-for-transcriptomics, one can find the reproducible code.
The tight synchronization of cellular actions is facilitated by the feedback mechanisms inherent within gene regulatory networks (GRNs). Despite this, genes inside a cell receive information from and send signals to cells that are next to them. Gene regulatory networks (GRNs) and cell-cell interactions (CCIs) deeply influence each other's function and behavior. Monogenetic models Various computational methods have been devised for the purpose of inferring gene regulatory networks operating within cellular environments. Techniques for inferring CCIs have advanced recently, utilizing single-cell gene expression data coupled with, or without, cell spatial location information. Nevertheless, in actuality, the two procedures are not isolated from one another and are beholden to spatial limitations. Despite this logical underpinning, there are currently no methods available for inferring both GRNs and CCIs using a single model.
Inputting GRNs and leveraging spatially resolved gene expression data, CLARIFY, the tool we present, computes CCIs and simultaneously outputs improved cell-specific GRNs. CLARIFY's innovative algorithm, a multi-level graph autoencoder, mimics cellular networks at a higher level of abstraction and, at a deeper level, cell-specific gene regulatory networks. We utilized CLARIFY on two authentic spatial transcriptomic datasets, one stemming from seqFISH and the other from MERFISH, and further evaluated it with simulated datasets provided by scMultiSim. A comparative analysis was undertaken of the quality of predicted gene regulatory networks (GRNs) and complex causal interactions (CCIs), utilizing baseline methods that concentrated on either exclusively GRNs or solely CCIs. The baseline is consistently outperformed by CLARIFY, as indicated by a comparison across commonly used evaluation metrics. Roxadustat modulator Our findings underscore the critical role of concurrent inference of CCIs and GRNs, and the utility of layered graph neural networks as an analytical tool for biological networks.
The repository https://github.com/MihirBafna/CLARIFY houses both the source code and the data.
The source code and data are accessible at https://github.com/MihirBafna/CLARIFY.
In the context of causal query estimation for biomolecular networks, the selection of a 'valid adjustment set'—a subset of network variables—is crucial to eliminate estimator bias. Valid adjustment sets, each possessing a different variance, may be yielded from a single query. Current methods for partially observed networks utilize graph-based criteria to pinpoint an adjustment set that minimizes the asymptotic variance.