AIMC Topic: Sequence Analysis, RNA

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BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.

BioMed research international
MicroRNAs (miRNAs) are a set of short (21-24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biologica...

TargetM6A: Identifying N-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

IEEE transactions on nanobioscience
As one of the most ubiquitous post-transcriptional modifications of RNA, N-methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly imp...

Transcriptomes of lineage-specific Drosophila neuroblasts profiled by genetic targeting and robotic sorting.

Development (Cambridge, England)
A brain consists of numerous distinct neurons arising from a limited number of progenitors, called neuroblasts in Drosophila. Each neuroblast produces a specific neuronal lineage. To unravel the transcriptional networks that underlie the development ...

MiRTDL: A Deep Learning Approach for miRNA Target Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) regulate genes that are associated with various diseases. To better understand miRNAs, the miRNA regulatory mechanism needs to be investigated and the real targets identified. Here, we present miRTDL, a new miRNA target prediction ...

Seq-ing improved gene expression estimates from microarrays using machine learning.

BMC bioinformatics
BACKGROUND: Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use...

Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum.

Nucleic acids research
Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-...

ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors.

BMC bioinformatics
BACKGROUND: microRNAs (miRNAs) play a vital role in development, oncogenesis, and apoptosis by binding to mRNAs to regulate the posttranscriptional level of coding genes in mammals, plants, and insects. Recent studies have demonstrated that the expre...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

Computers in biology and medicine
BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, and therapy resistance. However, there has been limited exploration of the role of LLPS in hepatoblas...

Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application.

Critical reviews in oncology/hematology
INTRODUCTION: Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors und...

GBMPurity: A machine learning tool for estimating glioblastoma tumor purity from bulk RNA-sequencing data.

Neuro-oncology
BACKGROUND: Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease...