13 research outputs found
Representation learning of drug and disease terms for drug repositioning
Drug repositioning (DR) refers to identification of novel indications for the
approved drugs. The requirement of huge investment of time as well as money and
risk of failure in clinical trials have led to surge in interest in drug
repositioning. DR exploits two major aspects associated with drugs and
diseases: existence of similarity among drugs and among diseases due to their
shared involved genes or pathways or common biological effects. Existing
methods of identifying drug-disease association majorly rely on the information
available in the structured databases only. On the other hand, abundant
information available in form of free texts in biomedical research articles are
not being fully exploited. Word-embedding or obtaining vector representation of
words from a large corpora of free texts using neural network methods have been
shown to give significant performance for several natural language processing
tasks. In this work we propose a novel way of representation learning to obtain
features of drugs and diseases by combining complementary information available
in unstructured texts and structured datasets. Next we use matrix completion
approach on these feature vectors to learn projection matrix between drug and
disease vector spaces. The proposed method has shown competitive performance
with state-of-the-art methods. Further, the case studies on Alzheimer's and
Hypertension diseases have shown that the predicted associations are matching
with the existing knowledge.Comment: Accepted to appear in 3rd IEEE International Conference on
Cybernetics (Spl Session: Deep Learning for Prediction and Estimation
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Graph neural networks (GNNs), in general, are built on the assumption of a
static set of features characterizing each node in a graph. This assumption is
often violated in practice. Existing methods partly address this issue through
feature imputation. However, these techniques (i) assume uniformity of feature
set across nodes, (ii) are transductive by nature, and (iii) fail to work when
features are added or removed over time. In this work, we address these
limitations through a novel GNN framework called GRAFENNE. GRAFENNE performs a
novel allotropic transformation on the original graph, wherein the nodes and
features are decoupled through a bipartite encoding. Through a carefully chosen
message passing framework on the allotropic transformation, we make the model
parameter size independent of the number of features and thereby inductive to
both unseen nodes and features. We prove that GRAFENNE is at least as
expressive as any of the existing message-passing GNNs in terms of
Weisfeiler-Leman tests, and therefore, the additional inductivity to unseen
features does not come at the cost of expressivity. In addition, as
demonstrated over four real-world graphs, GRAFENNE empowers the underlying GNN
with high empirical efficacy and the ability to learn in continual fashion over
streaming feature sets.Comment: 17 pages, 4 figures and 9 tables. Accepted in ICML 2023, DOI will be
updated once it is availabl
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Deep graph generative modeling has gained enormous attraction in recent years
due to its impressive ability to directly learn the underlying hidden graph
distribution. Despite their initial success, these techniques, like much of the
existing deep generative methods, require a large number of training samples to
learn a good model. Unfortunately, large number of training samples may not
always be available in scenarios such as drug discovery for rare diseases. At
the same time, recent advances in few-shot learning have opened door to
applications where available training data is limited. In this work, we
introduce the hitherto unexplored paradigm of few-shot graph generative
modeling. Towards this, we develop GSHOT, a meta-learning based framework for
few-shot labeled graph generative modeling. GSHOT learns to transfer
meta-knowledge from similar auxiliary graph datasets. Utilizing these prior
experiences, GSHOT quickly adapts to an unseen graph dataset through self-paced
fine-tuning. Through extensive experiments on datasets from diverse domains
having limited training samples, we establish that GSHOT generates graphs of
superior fidelity compared to existing baselines
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Graph partitioning aims to divide a graph into disjoint subsets while
optimizing a specific partitioning objective. The majority of formulations
related to graph partitioning exhibit NP-hardness due to their combinatorial
nature. As a result, conventional approximation algorithms rely on heuristic
methods, sometimes with approximation guarantees and sometimes without.
Unfortunately, traditional approaches are tailored for specific partitioning
objectives and do not generalize well across other known partitioning
objectives from the literature. To overcome this limitation, and learn
heuristics from the data directly, neural approaches have emerged,
demonstrating promising outcomes. In this study, we extend this line of work
through a novel framework, NeuroCut. NeuroCut introduces two key innovations
over prevailing methodologies. First, it is inductive to both graph topology
and the partition count, which is provided at query time. Second, by leveraging
a reinforcement learning based framework over node representations derived from
a graph neural network, NeuroCut can accommodate any optimization objective,
even those encompassing non-differentiable functions. Through empirical
evaluation, we demonstrate that NeuroCut excels in identifying high-quality
partitions, showcases strong generalization across a wide spectrum of
partitioning objectives, and exhibits resilience to topological modifications
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Optimization of atomic structures presents a challenging problem, due to
their highly rough and non-convex energy landscape, with wide applications in
the fields of drug design, materials discovery, and mechanics. Here, we present
a graph reinforcement learning approach, StriderNET, that learns a policy to
displace the atoms towards low energy configurations. We evaluate the
performance of StriderNET on three complex atomic systems, namely, binary
Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon.
We show that StriderNET outperforms all classical optimization algorithms and
enables the discovery of a lower energy minimum. In addition, StriderNET
exhibits a higher rate of reaching minima with energies, as confirmed by the
average over multiple realizations. Finally, we show that StriderNET exhibits
inductivity to unseen system sizes that are an order of magnitude different
from the training system
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Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action
Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice
LIMIP: Lifelong Learning to Solve Mixed Integer Programs
Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has gained attention due to its success in reducing the running time for solving MIPs. However, existing learning-to-branch methods assume that the entire training data is available in a single session of training. This assumption is often not true, and if the training data is supplied in continual fashion over time, existing techniques suffer from catastrophic forgetting. In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs. To mitigate catastrophic forgetting, we propose LIMIP, which is powered by the idea of modeling an MIP instance in the form of a bipartite graph, which we map to an embedding space using a bipartite Graph Attention Network. This rich embedding space avoids catastrophic forgetting through the application of knowledge distillation and elastic weight consolidation, wherein we learn the parameters key towards retaining efficacy and are therefore protected from significant drift. We evaluate LIMIP on a series of NP-hard problems and establish that in comparison to existing baselines, LIMIP is up to 50% better when confronted with lifelong learnin
On the Generalization of Neural Combinatorial Optimization Heuristics
Neural Combinatorial Optimization approaches have recently leveraged the
expressiveness and flexibility of deep neural networks to learn efficient
heuristics for hard Combinatorial Optimization (CO) problems. However, most of
the current methods lack generalization: for a given CO problem, heuristics
which are trained on instances with certain characteristics underperform when
tested on instances with different characteristics. While some previous works
have focused on varying the training instances properties, we postulate that a
one-size-fit-all model is out of reach. Instead, we formalize solving a CO
problem over a given instance distribution as a separate learning task and
investigate meta-learning techniques to learn a model on a variety of tasks, in
order to optimize its capacity to adapt to new tasks. Through extensive
experiments, on two CO problems, using both synthetic and realistic instances,
we show that our proposed meta-learning approach significantly improves the
generalization of two state-of-the-art models.Comment: Published in ECML PKDD 202