AIMC Topic: Transcriptome

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Comparative Analysis of the Cytology and Transcriptomes of the Cytoplasmic Male Sterility Line H276A and Its Maintainer Line H276B of Cotton (Gossypium barbadense L.).

International journal of molecular sciences
In this study, the tetrad stage of microspore development in a new cotton ( L.) cytoplasmic male sterility (CMS) line, H276A, was identified using paraffin sections at the abortion stage. To explore the molecular mechanism underlying CMS in cotton, a...

Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

The Analyst
The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from ind...

Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Artificial intelligence in medicine
OBJECTIVE: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).

Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help dev...

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

Cell systems
Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, ...

TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BMC bioinformatics
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimen...

Drug Target Prediction by Multi-View Low Rank Embedding.

IEEE/ACM transactions on computational biology and bioinformatics
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has...

Differential transcriptome analysis supports Rhodnius montenegrensis and Rhodnius robustus (Hemiptera, Reduviidae, Triatominae) as distinct species.

PloS one
Chagas disease is one of the main parasitic diseases found in Latin America and it is estimated that between six and seven million people are infected worldwide. Its etiologic agent, the protozoan Trypanosoma cruzi, is transmitted by triatomines, som...