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24 papers · ranked by Valyu relevance

01
BbioRxiv15 Jul 2025
Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features
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…
85relevance
02
BbioRxiv15 Jul 2025
Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features
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…
85relevance
03
BbioRxiv1 Feb 2025
Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks
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…
85relevance
04
AarXiv22 May 2017
Poincar\'e Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this…
83relevance
05
PPubMed4 Feb 2021
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
83relevance
06
BbioRxiv20 Dec 2018
SOM-based embedding improves efficiency of high-dimensional cytometry data analysis
Miroslav Kratochvíl, Abhishek Koladiya, Jana Balounova, Vendula Novosadova + 4 more
Efficient unbiased data analysis is a major challenge for laboratories handling large flow and mass cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analysis by providing high-performance embedding method for the cytometry data. The algorithm is designed for…
81relevance
07
PPubMed19 May 2020
Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]
Miroslav Kratochvíl, Abhishek Koladiya, Jiří Vondrášek
EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning…
80relevance
08
PPubMed28 Jun 2020
Propositionalization and embeddings: two sides of the same coin
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational…
80relevance
09
BbioRxiv1 Feb 2025
Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks
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…
79relevance
10
AarXiv2020
Improve Transformer Models with Better Relative Position Embeddings
Zhiheng Huang, Davis Liang, Peng Xu, Bing Xiang
Transformer architectures rely on explicit position encodings in order to preserve a notion of word order. In this paper, we argue that existing work does not fully utilize position information. For example, the initial proposal of a sinusoid embedding is fixed and not learnable. In this paper, we first review absolute…
78relevance
11
PPubMed5 Sep 2022
HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning
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…
78relevance
12
PPubMed16 Nov 2023
Graph embedding and geometric deep learning relevance to network biology and structural chemistry
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…
78relevance
13
CchemRxiv27 May 2024
Embedded Many-Body Green's Function Methods for Complex Molecular Systems
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…
78relevance
14
AarXiv25 Oct 2025
MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models
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…
78relevance
15
CchemRxiv19 Nov 2025
A General Pythonic Framework for DFT-in-DFT and WF-in-DFT Embedding
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…
77relevance
16
AarXiv17 Dec 2019
A Probabilistic approach for Learning Embeddings without Supervision
Ujjal Kr Dutta, Mehrtash Harandi, Chandra Sekhar Chellu
For challenging machine learning problems such as zero-shot learning and fine-grained categorization, embedding learning is the machinery of choice because of its ability to learn generic notions of similarity, as opposed to class-specific concepts in standard classification models. Embedding learning aims at learning…
77relevance
17
PPubMed15 Feb 2023
Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information
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…
76relevance
18
CchemRxiv14 Aug 2025
From QM/MM to ML/MM: A new era in multiscale modeling
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…
73relevance
19
AarXiv15 Dec 2023
Embedding Compression in Recommender Systems: A Survey
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…
73relevance
20
BbioRxiv25 Sep 2020
Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data
Kevin Z. Lin, Jing Lei, Kathryn Roeder
Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such non-linear embedding methods are often not well understood. In this article, we develop the eSVD…
73relevance
21
CchemRxiv17 Jul 2023
Subsystem Density-Functional Theory (Update)
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…
72relevance
22
AarXiv2021
Rethinking and Improving Relative Position Encoding for Vision Transformer
Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu + 1 more
Relative position encoding (RPE) is important for transformer to capture sequence ordering of input tokens. General efficacy has been proven in natural language processing. However, in computer vision, its efficacy is not well studied and even remains controversial, e.g., whether relative position encoding can work…
70relevance
23
CchemRxiv4 Oct 2024
From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics
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…
62relevance
24
CchemRxiv17 Jan 2025
AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics
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…
59relevance

Results for embedding methods

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Poincar\'e Embeddings for Learning Hierarchical Representations

Survey on graph embeddings and their applications to machine learning problems on graphs

SOM-based embedding improves efficiency of high-dimensional cytometry data analysis

Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]

Propositionalization and embeddings: two sides of the same coin

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Improve Transformer Models with Better Relative Position Embeddings

HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Embedded Many-Body Green's Function Methods for Complex Molecular Systems

MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models

A General Pythonic Framework for DFT-in-DFT and WF-in-DFT Embedding

A Probabilistic approach for Learning Embeddings without Supervision

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

From QM/MM to ML/MM: A new era in multiscale modeling

Embedding Compression in Recommender Systems: A Survey

Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data

Subsystem Density-Functional Theory (Update)

Rethinking and Improving Relative Position Encoding for Vision Transformer

From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Poincar\'e Embeddings for Learning Hierarchical Representations

Survey on graph embeddings and their applications to machine learning problems on graphs

SOM-based embedding improves efficiency of high-dimensional cytometry data analysis

Generalized EmbedSOM on quadtree-structured self-organizing maps [version 2; peer review: 2 approved]

Propositionalization and embeddings: two sides of the same coin

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Improve Transformer Models with Better Relative Position Embeddings

HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Embedded Many-Body Green's Function Methods for Complex Molecular Systems

MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models

A General Pythonic Framework for DFT-in-DFT and WF-in-DFT Embedding

A Probabilistic approach for Learning Embeddings without Supervision

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

From QM/MM to ML/MM: A new era in multiscale modeling

Embedding Compression in Recommender Systems: A Survey

Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data

Subsystem Density-Functional Theory (Update)

Rethinking and Improving Relative Position Encoding for Vision Transformer

From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics