Graphical representations provide us with a tool allowing visual inspection of the sequences. To visualize and compare different DNA sequences, a novel alignment-free method is proposed in this paper for both graphical representation and similarity analysis of sequences. We introduce a transformation to represent each DNA sequence with neighboring nucleotide matrix. Then, based on approximate joint diagonalization theory, we transform each DNA primary sequence into a corresponding eigenvalue vector (EVV), which can be considered as numerical characterization of DNA sequence. Meanwhile, we get graphical representation for DNA sequence via the plot of EVV in 2-D plane. Moreover, using k-means, we cluster these feature curves of sequences into several reasonable subclasses. In addition, similarity analyses are performed by computing the distances among the obtained vectors. This approach contains more sequence information, and it analyzes all the involved sequence information jointly rather than separately. A typical dendrogram constructed by this method demonstrates the effectiveness of our approach.
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http://dx.doi.org/10.1109/titb.2012.2227146 | DOI Listing |
Heliyon
January 2025
Institute of Mathematics, Henan Academy of Sciences, Zhengzhou, 450046, China.
This study examines the behavior of the Casson nanofluid bioconvection flow around a spinning disc under various influences, including gyrotactic microorganisms, multiple slips, and thermal radiation. Notably, it accounts for the reversible nature of the flow and incorporates the esterification process. The aim of this study is to investigate the influence of reversible chemical reactions on the flow behavior of a Casson nanofluid in the presence of bioconvective microorganisms over a spinning disc.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
MethodsX
June 2025
Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
In infectious disease outbreak modeling, there remains a gap in addressing spatiotemporal challenges present in established models. This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
In applied research, fractional calculus plays an important role for comprehending a wide range of intricate physical phenomena. One of the Klein-Gordon model's peculiar case yields the Phi-four equation. Additionally, throughout the past few decades it has been utilized to explain the kink and anti-kink solitary waveform contacts that occur in biological systems and in the field of nuclear mechanics.
View Article and Find Full Text PDFPLoS One
January 2025
Biology Department, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.
This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator.
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