A novel data augmentation approach for influenza A subtype prediction based on HA proteins.

Influenza, a pervasive viral respiratory illness, remains a significant global health concern. The influenza A virus, capable of causing pandemics, necessitates timely identification of specific subtypes for effective prevention and control, as highlighted by the World Health Organization. The genetic diversity of influenza A virus, especially in the hemagglutinin protein, presents challenges for accurate subtype prediction.

BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representation.

Drug failure during experimental procedures due to low bioactivity presents a significant challenge. To mitigate this risk and enhance compound bioactivities, predicting bioactivity classes during lead optimization is essential. The existing studies on structure-activity relationships have highlighted the connection between the chemical structures of compounds and their bioactivity.

Exploring ceRNA networks for key biomarkers in breast cancer subtypes and immune regulation.

Breast cancer is a major global health concern, and recent researches have highlighted the critical roles of non-coding RNAs in both cancer and the immune system. The competing endogenous RNA hypothesis suggests that various types of RNA, including coding and non-coding RNAs, compete for microRNA targets, acting as molecular sponges. This study introduces the Pre_CLM_BCS pipeline to investigate the potential of long non-coding RNAs and circular RNAs as biomarkers in breast cancer subtypes.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.

Background: Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.

Motivation: Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Biomarker prediction in autism spectrum disorder using a network-based approach.

Background: Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis.

Protein sequence profile prediction using ProtAlbert transformer.

Profiles are used to model protein families and domains. They are built by multiple sequence alignments obtained by mapping a query sequence against a database to generate a profile based on the substitution scoring matrix. The profile applications are very dependent on the alignment algorithm and scoring system for amino acid substitution.

The outward shift of clarithromycin binding to the ribosome in mutant Helicobacter pylori strains.

Objectives: Disruption of protein synthesis, by drug-mediated restriction of the ribosomal nascent peptide exit tunnel (NPET), may inhibit bacterial growth. Here, we have studied the secondary and tertiary structures of domain V of the 23S rRNA in the wild-type and mutant (resistant) H. pylori strains and their mechanisms of interaction with clarithromycin (CLA).

The assessment of histone acetylation marks in the vicinity of transcription factor binding sites in human CD4 + T cells using information theory methods.

The genetic information encoded in structural genes is decoded by an intracellular process called gene expression. This mechanism is regulated by epigenetic processes such as histone acetylation. Histone acetylation, which happens in nucleosomes, exposes DNA (genome) to transcription factors.

Draft Genome Sequence of Pseudomonas aeruginosa Strain LMG 1272, an Atypical White Line Reaction Producer.

The draft genome sequence of LMG 1272, isolated from mushroom, is reported here. This strain triggers formation of a precipitate ("white line") when cocultured with However, LMG 1272 lacks the capacity to produce a cyclic lipopeptide that is typically associated with white line formation, suggesting the involvement of a different diffusible factor.

A Stochastic Model of DNA Double-Strand Breaks Repair Throughout the Cell Cycle.

Cell cycle phase is a decisive factor in determining the repair pathway of DNA double-strand breaks (DSBs) by non-homologous end joining (NHEJ) or homologous recombination (HR). Recent experimental studies revealed that 53BP1 and BRCA1 are the key mediators of the DNA damage response (DDR) with antagonizing roles in choosing the appropriate DSB repair pathway in G1, S, and G2 phases. Here, we present a stochastic model of biochemical kinetics involved in detecting and repairing DNA DSBs induced by ionizing radiation during the cell cycle progression.

The assessment of efficient representation of drug features using deep learning for drug repositioning.

Background: De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases.

Assessing the impact of exact reads on reducing the error rate of read mapping.

Background: Nowadays, according to valuable resources of high-quality genome sequences, reference-based assembly methods with high accuracy and efficiency are strongly required. Many different algorithms have been designed for mapping reads onto a genome sequence which try to enhance the accuracy of reconstructed genomes. In this problem, one of the challenges occurs when some reads are aligned to multiple locations due to repetitive regions in the genomes.

The effect of stochasticity on repair of DNA double strand breaks throughout non-homologous end joining pathway.

DNA double strand breaks (DSBs) are the most lethal lesions of DNA induced by ionizing radiation, industrial chemicals and a wide variety of drugs used in chemotherapy. In the context of DNA damage response system modelling, uncertainty may arise in several ways such as number of induced DSBs, kinetic rates and measurement error in observable quantities. Therefore, using the stochastic approaches is imperative to gain further insight into the dynamic behaviour of DSBs repair process.

Evaluating the quality of SHAPE data simulated by k-mers for RNA structure prediction.

Finding an effective measure to predict a more accurate RNA secondary structure is a challenging problem. In the last decade, an experimental method, known as selective [Formula: see text]-hydroxyl acylation analyzed by primer extension (SHAPE), was proposed to measure the tendency of forming a base pair for almost all nucleotides in an RNA sequence. These SHAPE reactivities are then utilized to improve the accuracy of RNA structure prediction.

S-FLN: A sequence-based hierarchical approach for functional linkage network construction.

The functional linkage network (FLN) construction is a primary and important step in drug discovery and disease gene prioritization methods. In order to construct FLN, several methods have been introduced based on integration of various biological data. Although, there are impressive ideas behind these methods, they suffer from low quality of the biological data.

RNA design using simulated SHAPE data.

It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research.

Evolutionary Algorithm for RNA Secondary Structure Prediction Based on Simulated SHAPE Data.

Background: Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure.

Evaluating the accuracy of protein design using native secondary sub-structures.

Background: According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. In spite of IPF essential applications, it has not been investigated as much as PSP problem. In fact, the ultimate goal of IPF problem or protein design is to create proteins with enhanced properties or even novel functions.

Corrigendum: MWCOACH: Protein complex prediction in weighted PPI networks [Genes Genet. Syst. (2015) 90, p. 317-324].

"J-STAGE Advance published date: 15 January 2015" on p. 317 should be changed to "J-STAGE Advance published date: 15 January 2016".

WCOACH: Protein complex prediction in weighted PPI networks.

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates.

RNA secondary structure prediction based on SHAPE data in helix regions.

RNA molecules play important and fundamental roles in biological processes. Frequently, the functional form of single-stranded RNA molecules requires a specific tertiary structure. Classically, RNA structure determination has mostly been accomplished by X-Ray crystallography or Nuclear Magnetic Resonance approaches.

Using temperature effects to predict the interactions between two RNAs.

Motivation: Interaction of two RNA molecules is considered as an important factor that regulates gene expression post-transcriptional process. Most of the ncRNAs prevent the translation of their target mRNA(s) by forming stable bindings with them. Although several computational methods have been proposed to predict the interactions between two RNAs, none of them can produce reliable and accurate results.

RNA-RNA interaction prediction using genetic algorithm.

Background: RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them.