AI Medical Compendium Topic

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Sequence Analysis, RNA

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JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.

PLoS computational biology
With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have beco...

DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.

Genes
As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurat...

Fast and precise single-cell data analysis using a hierarchical autoencoder.

Nature communications
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...

Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture.

IEEE/ACM transactions on computational biology and bioinformatics
CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea...

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing.

Communications biology
Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. H...

Deep learning predicts short non-coding RNA functions from only raw sequence data.

PLoS computational biology
Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has dem...

A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data.

Nature biomedical engineering
Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for d...

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.

Methods (San Diego, Calif.)
Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as ...

Large-Scale Multi-omic Analysis of COVID-19 Severity.

Cell systems
We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associa...

LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.

Molecules (Basel, Switzerland)
Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related...