1,277 research outputs found
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data
Long non-coding RNA, microRNA, and messenger RNA enable key regulations of
various biological processes through a variety of diverse interaction
mechanisms. Identifying the interactions and cross-talk between these
heterogeneous RNA classes is essential in order to uncover the functional role
of individual RNA transcripts, especially for unannotated and newly-discovered
RNA sequences with no known interactions. Recently, sequence-based deep
learning and network embedding methods are becoming promising approaches that
can either predict RNA-RNA interactions from a sequence or infer missing
interactions from patterns that may exist in the network topology. However, the
majority of these methods have several limitations, eg, the inability to
perform inductive predictions, to distinguish the directionality of
interactions, or to integrate various sequence, interaction, and annotation
biological datasets. We proposed a novel deep learning-based framework,
rna2rna, which learns from RNA sequences to produce a low-dimensional embedding
that preserves the proximities in both the interactions topology and the
functional affinity topology. In this proposed embedding space, we have
designated a two-part" source and target contexts" to capture the targeting and
receptive fields of each RNA transcript, while encapsulating the heterogenous
cross-talk interactions between lncRNAs and miRNAs. From experimental results,
our method exhibits superior performance in AUPR rates compared to state-of-art
approaches at predicting missing interactions in different RNA-RNA interaction
databases and was shown to accurately perform link predictions to novel RNA
sequences not seen at training time, even without any prior information.
Additional results suggest that our proposed framework can capture a manifold
for heterogeneous RNA sequences to discover novel functional annotations
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling
Network embedding techniques inspired by word2vec represent an effective
unsupervised relational learning model. Commonly, by means of a Skip-Gram
procedure, these techniques learn low dimensional vector representations of the
nodes in a graph by sampling node-context examples. Although many ways of
sampling the context of a node have been proposed, the effects of the way a
node is chosen have not been analyzed in depth. To fill this gap, we have
re-implemented the main four word2vec inspired graph embedding techniques under
the same framework and analyzed how different sampling distributions affects
embeddings performance when tested in node classification problems. We present
a set of experiments on different well known real data sets that show how the
use of popular centrality distributions in sampling leads to improvements,
obtaining speeds of up to 2 times in learning times and increasing accuracy in
all cases
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