Travel demand modeling is an essential tool in urban planning and transportation system management. Existing practically efficient algorithms for solving multistage travel demand problems are variations of the heurist...
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Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. ...
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Network intrusion detection, a well-explored cybersecurity field, has predominantly relied on supervised learning algorithms in the past two decades. However, their limitations in detecting only known anomalies prompt...
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Predicting the affinity between two proteins is one of the most relevant challenges in bioinformatics and one of the most useful for biotechnological and pharmaceutical applications. Current prediction methods use the...
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ISBN:
(纸本)9783031368042;9783031368059
Predicting the affinity between two proteins is one of the most relevant challenges in bioinformatics and one of the most useful for biotechnological and pharmaceutical applications. Current prediction methods use the structural information of the interaction complexes. However, predicting the structure of proteins requires enormous computational costs. Machine learning methods emerge as an alternative to this bioinformatics challenge. There are predictive methods for protein affinity based on structural information. However, for linear information, there are no development guidelines for elaborating predictive models, being necessary to explore several alternatives for processing and developing predictive models. This work explores different options for building predictive protein interaction models via deep learning architectures and classical machine learning algorithms, evaluating numerical representation methods and transformation techniques to represent structural complexes using linear information. Six types of predictive tasks related to the affinity and mutational variant evaluations and their effect on the interaction complex were explored. We show that classical machine learning and convolutional network-based methods perform better than graph convolutional network methods for studying mutational variants. In contrast, graph-based methods perform better on affinity problems or association constants, using only the linear information of the protein sequences. Finally, we show an illustrative use case, expose how to use the developed models, discuss the limitations of the explored methods and comment on future development strategies for improving the studied processes.
Efficient control of dynamic systems that interact with unstable immersions is of utmost importance across multiple domains, encompassing the stabilization of turbulent flows, generation of signals in radio engineerin...
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The proceedings contain 37 papers. The special focus in this conference is on Mathematical Software. The topics include: Computing the Determinant of a Dense Matrix over Z;FastECPP over MPI;at...
ISBN:
(纸本)9783031645280
The proceedings contain 37 papers. The special focus in this conference is on Mathematical Software. The topics include: Computing the Determinant of a Dense Matrix over Z;FastECPP over MPI;attacking a Levelled Fully Homomorphic Encryption System with Topological Data Analysis;formalising Families of -adic Galois Representations in Lean 4;formalization of the Existence of Frobenius Elements;formalising Analysis in Lean: Compactness and Dimensionality;formalisation of the Category of Hopf Algebras in Lean4;computing the Group of an Algebraic Variety over a Finite Field;computer Classification of Linear Codes Based on Lattice Point Enumeration;software for Proper Vertex-Colouring Exploiting Graph Symmetry;localization in Gromov—Witten Theory of Toric Varieties in a Computer Algebra System;massively Parallel methods for Free Resolutions;towards Parallel methods in Birational Geometry;towards Parallel algorithms for Gromov-Witten Invariants of Elliptic Curves;a SageMath Package for Elementary and Sign Vectors with Applications to Chemical Reaction Networks;Symbolic Integration Algorithm Selection with Machine Learning: LSTMs Vs Tree LSTMs;constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems;exploring Alternative Machine Learning models for Variable Ordering in Cylindrical Algebraic Decomposition;machine Learning for Number Theory: Unsupervised Learning with L-Functions;approximation of an Inverse of the Incomplete Beta Function;DLMF Standard Reference Tables on Demand;Integrating Mathematical Data and Resources: Advancements in zbMATH Open for Enhanced Mathematical Research Accessibility and Reproducibility;A FAIR File Format for Mathematical Software;predefined Software Environment Runtimes as a Measure for Reproducibility;Towards a FAIR Documentation of Workflows and models in Applied Mathematics;monodromy Coordinates;effective Alpha Theory Certification Using Interval Arithmetic: Alpha Theory over Regions;gröbner Degen
Our research addresses the pressing issue of congestive heart failure (CHF), a critical cardiovascular condition characterized by the heart’s diminished ability to pump blood effectively, resulting in fluid accumulat...
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Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the dat...
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The proceedings contain 25 papers. The special focus in this conference is on Modelling and Simulation for Autonomous Systems. The topics include: Atlas Fusion 2.0 A ROS2 Based Real-Time Sensor Fusion Framework;UAS Fl...
ISBN:
(纸本)9783031713965
The proceedings contain 25 papers. The special focus in this conference is on Modelling and Simulation for Autonomous Systems. The topics include: Atlas Fusion 2.0 A ROS2 Based Real-Time Sensor Fusion Framework;UAS Flight Path Optimization Model for Effective Monitoring and Surveillance of the Buffer Zone in the UNFICYP Peacekeeping Mission;A Model-Based Design Approach for a System of Systems Based on an Integrated UAV Platform;practical Applicability of Tree Spacing Passability Analysis on Vehicle Path Planning;where to Go and How to Get There: Tactical Terrain Analysis for Military Unmanned Ground-Vehicle Mission Planning;a Survey of Trajectory Planning algorithms for Off-Road Uncrewed Ground Vehicles;multi-physics and Multi-spectral Sensors Simulator for Autonomous Flight Functions Development;Conceptual Aspects of Counter-UAS Modelling and Simulation;challenges Associated with the Deployment of Autonomous Reconnaissance Systems on Future Battlefields;The Key Challenges of SBAD M development of Geoprocessing Tool for Wet Gap Crossing in Military Operations;digital Twin Modeling for Machine Vision Testing in Autonomous Systems;a Situation Analysis Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations Under Risk and Uncertainty: Concept and First Implementations;a Tactical Planning Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations: Concept and First Implementations;survey on Sensing, Modelling and Reasoning Aspects in Military Autonomous Systems;Camera Based AI models Used with LiDAR Data for Improvement of Detected Object Parameters;the Analysis of Point Cloud Registration methods for Natural Environment in Autonomous Driving;Hyperspectral Data Dimensionality Reduction: A Comparative Study Between PCA and Autoencoder methods.
Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that co...
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