26 papers · ranked by Valyu relevance
Nikolaos Fanourakis, Vasilis Efthymiou, Dimitris Kotzinos, Vassilis Christophides
'Vassilis Christophides'] In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world…
Mattia Cervellini, Blerina Sinaimeri, Catherine Matias, Alessio Martino
Metabolic networks are complex systems that describe the bio-chemical reactions within an organism through pairwise interactions between chemical compounds. While this representation is widely used to study biolog-ical function, it fails to capture the full structure of metabolic reactions, many of which involve more…
Mattia Cervellini, Blerina Sinaimeri, Catherine Matias, Alessio Martino
Metabolic networks are complex systems that describe the biochemical reactions within an organism through pairwise interactions between chemical compounds. While this representation is widely used to study biological function, it fails to capture the full structure of metabolic reactions, many of which involve more…
Jeffrey Zhong, Lechuan Li, Ruth Dannenfelser, Vicky Yao
Gene embeddings have emerged as transformative tools in computational biology, enabling the efficient translation of complex biological datasets into compact vector representations. This study presents a comprehensive benchmark by evaluating 38 classic and state-of-the-art gene embedding methods across a spectrum of…
Zhijin Guo, Zhaozhen Xu, Martha Lewis, Nello Cristianini
Embeddings in AI convert symbolic structures into fixed-dimensional vectors, effectively fusing multiple signals. However, the nature of this fusion in real-world data is often unclear. To address this, we introduce two methods: (1) Correlation-based Fusion Detection, measuring correlation between known attributes and…
Mustafa Temiz, Burcu Bakir-Gungor, Pınar Güner Şahan, Mustafa Coskun + 1 more
'Rajesh Bhardwaj'] Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to…
Yasamin Ghahremani, Vangelis Metsis
Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. In this paper, we present a comprehensive review and evaluation of time series embedding methods for effective representations in machine learning and deep learning models. We introduce a taxonomy…
Kris Sankaran, Shuzhen Zhang, Chenab, Marina Meilă
Nonlinear dimensionality reduction methods like UMAP and t-SNE can help to organize high-dimensional genomics data into manageable low-dimensional representations, like cell types or differentiation trajectories. Such reductions can be powerful, but inevitably introduce distortion. A growing body of work has…
John A. Lees, Gerry Tonkin-Hill, Zhirong Yang, Jukka Corander
In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species and the number of genomes is expected…
Kris Sankaran, Shuzhen Zhang, Chenab, Marina Meilă
Nonlinear dimensionality reduction methods like UMAP and t-SNE can help to organize high-dimensional genomics data into manageable low-dimensional representations, like cell types or differentiation trajectories. Such reductions can be powerful, but inevitably introduce distortion. A growing body of work has…
Jeffrey Zhong, Lechuan Li, Ruth Dannenfelser, Vicky Yao
Accurate, data-driven representations of genes are critical for interpreting high-throughput biological data, yet no consensus exists on the most effective embedding strategy for common functional prediction tasks. Here, we present a systematic comparison of 38 gene embedding methods derived from amino acid sequences…
Ling Cai, Krzysztof Janowicz, Rui Zhu, Gengchen Mai + 2 more
Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning…
Paola Lecca, Michela Lecca
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually…
Gianluca Tirimbo, Vivek Sundaram, Björn Baumeier
Many-body Green's function theory in the GW approximation with the Bethe--Salpeter equation (BSE) provides a powerful framework for the first-principles calculations of single-particle and electron-hole excitations in perfect crystals and molecules alike. Application to complex molecular systems, e.g., solvated dyes…
Simchoni, Giora, Rosset, Saharon
We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. By treating embeddings as latent random effects within a variational autoencoder framework, our…
Authors not listed
High-level quantum mechanical (QM) simulations provide accurate electronic information of chemical systems but scale unfavourably with system size, making calculations of applied systems challenging. Hierarchical quantum mechanics in quantum mechanics embedding (QM/QM) addresses this issue by localising the highly…
Xiaoli Huang, Haibo Chen, Zheng Zhang, Donald J. Jacobs + 1 more
'Sotiris Kotsiantis'] Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors…
Deepa D. Shankar, Adresya Suresh Azhakath
The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet…
Shiwei Li, Huifeng Guo, Xing Tang, Ruiming Tang + 3 more
'Ruixuan Li' 'Rui Zhang'] To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued…
Christoph Jacob, Johannes Neugebauer
The past years since the publication of our review on subsystem density-functional theory (sDFT) [WIREs Comput. Mol. Sci. 2014, 4:325--362] have witnessed a rapid development and diversification of quantum mechanical fragmentation and embedding approaches related to sDFT and frozen-density embedding (FDE). In this…
Authors not listed
Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum desription with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding…
Bhargavi Mokashi, Vandana S. Bhat, Jagadeesh D. Pujari, S. Roopashree + 2 more
'S. Roopashree' 'T. R. Mahesh' 'D. Stalin Alex'] In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced…
Qingjun Wang, Qilong Zhang, Xiaoying Song, Yiwen Liu
Reversible data hiding (RDH) is a crucial information hiding technique for copyright protection and integrity verification of digital media, as it ensures the original content can be perfectly restored. However, current research on RDH remains predominantly focused on images, and relatively little attention has been…
Kirill Zinovjev, Lester Hedges, Rubén Montagud Andreu, Christopher Woods + 2 more
We present in this work the emle-engine package (https://github.com/chemle/emle-engine) – the implementation of a new machine learning embedding scheme for hybrid machine learning potential / molecular mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Authors not listed
Elucidating Collective Variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to…