7,097 research outputs found
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Gode -- Integrating Biochemical Knowledge Graph into Pre-training Molecule Graph Neural Network
The precise prediction of molecular properties holds paramount importance in
facilitating the development of innovative treatments and comprehending the
intricate interplay between chemicals and biological systems. In this study, we
propose a novel approach that integrates graph representations of individual
molecular structures with multi-domain information from biomedical knowledge
graphs (KGs). Integrating information from both levels, we can pre-train a more
extensive and robust representation for both molecule-level and KG-level
prediction tasks with our novel self-supervision strategy. For performance
evaluation, we fine-tune our pre-trained model on 11 challenging chemical
property prediction tasks. Results from our framework demonstrate our
fine-tuned models outperform existing state-of-the-art models.Comment: It's an ongoing work. We're exploring the ability of Gode on other
task
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained
types is often cast as prediction into a flat set of entity and type labels,
neglecting the rich hierarchies over types and entities contained in curated
ontologies. Previous attempts to incorporate hierarchical structure have
yielded little benefit and are restricted to shallow ontologies. This paper
presents new methods using real and complex bilinear mappings for integrating
hierarchical information, yielding substantial improvement over flat
predictions in entity linking and fine-grained entity typing, and achieving new
state-of-the-art results for end-to-end models on the benchmark FIGER dataset.
We also present two new human-annotated datasets containing wide and deep
hierarchies which we will release to the community to encourage further
research in this direction: MedMentions, a collection of PubMed abstracts in
which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet,
which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k
entity types. In experiments on all three datasets we show substantial gains
from hierarchy-aware training.Comment: ACL 201
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Microbial community pattern detection in human body habitats via ensemble clustering framework
The human habitat is a host where microbial species evolve, function, and
continue to evolve. Elucidating how microbial communities respond to human
habitats is a fundamental and critical task, as establishing baselines of human
microbiome is essential in understanding its role in human disease and health.
However, current studies usually overlook a complex and interconnected
landscape of human microbiome and limit the ability in particular body habitats
with learning models of specific criterion. Therefore, these methods could not
capture the real-world underlying microbial patterns effectively. To obtain a
comprehensive view, we propose a novel ensemble clustering framework to mine
the structure of microbial community pattern on large-scale metagenomic data.
Particularly, we first build a microbial similarity network via integrating
1920 metagenomic samples from three body habitats of healthy adults. Then a
novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is
proposed and applied onto the network to detect clustering pattern. Extensive
experiments are conducted to evaluate the effectiveness of our model on
deriving microbial community with respect to body habitat and host gender. From
clustering results, we observed that body habitat exhibits a strong bound but
non-unique microbial structural patterns. Meanwhile, human microbiome reveals
different degree of structural variations over body habitat and host gender. In
summary, our ensemble clustering framework could efficiently explore integrated
clustering results to accurately identify microbial communities, and provide a
comprehensive view for a set of microbial communities. Such trends depict an
integrated biography of microbial communities, which offer a new insight
towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
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