25,978 research outputs found
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
DDI Prediction via Heterogeneous Graph Attention Networks
Polypharmacy, defined as the use of multiple drugs together, is a standard
treatment method, especially for severe and chronic diseases. However, using
multiple drugs together may cause interactions between drugs. Drug-drug
interaction (DDI) is the activity that occurs when the impact of one drug
changes when combined with another. DDIs may obstruct, increase, or decrease
the intended effect of either drug or, in the worst-case scenario, create
adverse side effects. While it is critical to detect DDIs on time, it is
timeconsuming and expensive to identify them in clinical trials due to their
short duration and many possible drug pairs to be considered for testing. As a
result, computational methods are needed for predicting DDIs. In this paper, we
present a novel heterogeneous graph attention model, HAN-DDI to predict
drug-drug interactions. We create a heterogeneous network of drugs with
different biological entities. Then, we develop a heterogeneous graph attention
network to learn DDIs using relations of drugs with other entities. It consists
of an attention-based heterogeneous graph node encoder for obtaining drug node
representations and a decoder for predicting drug-drug interactions. Further,
we utilize comprehensive experiments to evaluate of our model and to compare it
with state-of-the-art models. Experimental results show that our proposed
method, HAN-DDI, outperforms the baselines significantly and accurately
predicts DDIs, even for new drugs.Comment: 10 pages, 3 figures, 8 tables, accepted in BioKD
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