Self-Attention Based Models for the Extraction of Molecular Interactions from Biological Texts

Srivastava, Prashant; Bej, Saptarshi; Yordanova, Kristina

doi:10.20944/preprints202110.0184.v1

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…In order to effectively extract the crucial information in the image, the results proved that this method is feasible. Srivastava et al introduced the attention mechanism to propose a special architecture based on neural networks, which was applied in biology [13]. Wang et al proposed a neural network model based on the attention mechanism, allowing important words to obtain higher weights, and then optimizing the objective function to significantly improve the effect [14].…”

Section: Introductionmentioning

confidence: 99%

Traffic flow prediction based on transformer

Zhang

2023

J. Phys.: Conf. Ser.

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Nowadays, it is an obvious fact that with the double blessing of economic growth and the diversity of human’s life, travel has become an indispensable activity during the leisure time. Meanwhile, due to the convenience, timesaving and accessibility, private cars gradually play a vital role on the road. However, it is conceivable that the increase in vehicles means that there will be inevitable congestion, especially during the peak hours of the day. In order to reverse the situation that private cars are from “convenience” to “inconvenience”, this paper decides to explore how to accurately predict the change of traffic flow over time, perceive the upcoming peak period in advance to provide reliable traffic information for publishing and allocate traffic resources reasonably. To capture the time features in traffic flow data, extract their internal laws and process them more accurately, this research applies a combined model of convolutional neural network (CNN) and transformer, that is, CNN is used for the feature extraction, and the extracted features are passed to the transformer model for further predictive analysis. The experimental dataset in the research comes from the official website of Highway England. The result displays that the iteration speed of this model is rapid, the prediction error is able to be minimized and it can be applied in the case of a huge dataset.

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Section: Introductionmentioning

confidence: 99%

Traffic flow prediction based on transformer

Zhang

2023

J. Phys.: Conf. Ser.

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A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites

2022

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By denaturing proteins and promoting the formation of multiprotein complexes, protein phosphorylation has important effects on the activity of protein functional molecules and cell signaling. The regulation of protein phosphorylation allows microbes to respond rapidly and reversibly to specific environmental stimuli or niches, which is closely related to the molecular mechanisms of bacterial drug resistance. Accurate prediction of phosphorylation sites (p-site) of prokaryotes can contribute to addressing bacterial resistance and providing new perspectives for developing novel antibacterial drugs. Most existing studies focus on human phosphorylation sites, while tools targeting phosphorylation site identification of prokaryotic proteins are still relatively scarce. This study designs a capsule network-based prediction technique for p-site in prokaryotes. To address the poor scalability and unreliability of dynamic routing processes in the output space of capsule networks, a more reliable way is introduced to learn the consistency between capsules. We incorporate a self-attention mechanism into the routing algorithm to capture the global information of the capsule, reducing the computational effort while enriching the representation capability of the capsule. Aiming at the weak robustness of the model, EcapsP improves the prediction accuracy and stability by introducing shortcuts and unconditional reconfiguration. In addition, the study compares and analyzes the prediction performance based on word vectors, physicochemical properties, and mixing characteristics in predicting serine (Ser/S), threonine (Thr/T), and tyrosine (Tyr/Y) p-site. The comprehensive experimental results show that the accuracy of the developed technique is close to 70% for the identification of the three phosphorylation sites in prokaryotes. Importantly, in side-by-side comparisons with other state-of-the-art predictors, our method improves the Matthews correlation coefficient (MCC) by approximately 7%. The results demonstrate the superiority of EcapsP in terms of high performance and reliability.

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Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

Bongrand

2022

CIMB

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An important goal of biological research is to explain and hopefully predict cell behavior from the molecular properties of cellular components. Accordingly, much work was done to build extensive “omic” datasets and develop theoretical methods, including computer simulation and network analysis to process as quantitatively as possible the parameters contained in these resources. Furthermore, substantial effort was made to standardize data presentation and make experimental results accessible to data scientists. However, the power and complexity of current experimental and theoretical tools make it more and more difficult to assess the capacity of gathered parameters to support optimal progress in our understanding of cell function. The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions. Recent experimental studies on important cell functions, such as adhesion and processing of environmental cues for decision-making, support the suggestion that it should be rewarding to complement standard binding properties such as affinity and kinetic constants, or even force dependence, with less frequently used parameters such as conformational flexibility or size of binding molecules.

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Self-Attention Based Models for the Extraction of Molecular Interactions from Biological Texts

Cited by 5 publications

References 5 publications

Traffic flow prediction based on transformer

Traffic flow prediction based on transformer

A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites

Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

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