Sarwan Ali
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View article: Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis Open
Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationships and hierarchical structures of sequence data when working…
View article: Performance of PTFE-Based Adaptive Building Facades for Climate Resilience: A Simulation-Driven Analysis
Performance of PTFE-Based Adaptive Building Facades for Climate Resilience: A Simulation-Driven Analysis Open
As an aesthetic architectural thermal barrier, the building envelope is considered vital and contributes substantially in improving the overall building performance. Responsive Facades bring in a revolutionary transformation to the static …
View article: Performance Evaluation of Etfe and Auto Heal for Responsive Kinetic Facades: Improving Building Efficiency and Urban Built Environment
Performance Evaluation of Etfe and Auto Heal for Responsive Kinetic Facades: Improving Building Efficiency and Urban Built Environment Open
Facades are integral to building design and their functionality, serving as the aesthetic outer shell and simultaneously acting as a thermal barrier. The Responsive Kinetic Facades revolutionize the concept of facades changing the dynamism…
View article: Sequence Analysis Using the Bezier Curve
Sequence Analysis Using the Bezier Curve Open
The analysis of sequences (e.g., protein, DNA, and SMILES string) is essential for disease diagnosis, biomaterial engineering, genetic engineering, and drug discovery domains. Conventional analytical methods focus on transforming sequences…
View article: Adapting Restorative Urban Design, for Open Spaces Towards, Thermal Heat Island, Reaching Human Mental Health and Well-being, Case study: Qaitbay Citadel Plaza, Alexandria, Egypt
Adapting Restorative Urban Design, for Open Spaces Towards, Thermal Heat Island, Reaching Human Mental Health and Well-being, Case study: Qaitbay Citadel Plaza, Alexandria, Egypt Open
Nowadays rapidly evolving urban landscape. These challenges increasingly impact residents' mental health and well-being. Overcrowding, noise and air pollution, long commutes, the size of the built environment, landscape and green areas, an…
View article: Improved Graph-Based Antibody-Aware Epitope Prediction with Protein Language Model-Based Embeddings
Improved Graph-Based Antibody-Aware Epitope Prediction with Protein Language Model-Based Embeddings Open
The accurate identification of B-cell epitopes is critical in antibody design, diagnostics, and immunotherapies. Many in silico approaches have recently been proposed to predict epitopes, but these approaches struggle primarily because of …
View article: Converting Time Series Data to Numeric Representations Using Alphabetic Mapping and k-mer strategy
Converting Time Series Data to Numeric Representations Using Alphabetic Mapping and k-mer strategy Open
In the realm of data analysis and bioinformatics, representing time series data in a manner akin to biological sequences offers a novel approach to leverage sequence analysis techniques. Transforming time series signals into molecular sequ…
View article: Hilbert Curve Based Molecular Sequence Analysis
Hilbert Curve Based Molecular Sequence Analysis Open
Accurate molecular sequence analysis is a key task in the field of bioinformatics. To apply molecular sequence classification algorithms, we first need to generate the appropriate representations of the sequences. Traditional numeric seque…
View article: Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences
Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences Open
The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by res…
View article: Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm
Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm Open
In molecular structure data, SMILES (Simplified Molecular Input Line Entry System) strings are used to analyze molecular structure design. Numerical feature representation of SMILES strings is a challenging task. This work proposes a kerne…
View article: DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging Open
Federated Learning (FL) is a distributed learning technique that maintains data privacy by providing a decentralized training method for machine learning models using distributed big data. This promising Federated Learning approach has als…
View article: EPIC: Enhancing Privacy through Iterative Collaboration
EPIC: Enhancing Privacy through Iterative Collaboration Open
Advancements in genomics technology lead to a rising volume of viral (e.g., SARS-CoV-2) sequence data, resulting in increased usage of machine learning (ML) in bioinformatics. Traditional ML techniques require centralized data collection a…
View article: TCellR2Vec: efficient feature selection for TCR sequences for cancer classification
TCellR2Vec: efficient feature selection for TCR sequences for cancer classification Open
Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient’s immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T…
View article: MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering Open
The t-Distributed Stochastic Neighbor Embedding (t-SNE) has emerged as a popular dimensionality reduction technique for visualizing high-dimensional data. It computes pairwise similarities between data points by default using an RBF kernel…
View article: Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks
Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks Open
Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to policy development. An important and popular technique for ex…
View article: Preserving Hidden Hierarchical Structure: Poincaré Distance for Enhanced Genomic Sequence Analysis
Preserving Hidden Hierarchical Structure: Poincaré Distance for Enhanced Genomic Sequence Analysis Open
The analysis of large volumes of molecular (genomic, proteomic, etc.) sequences has become a significant research field, especially after the recent coronavirus pandemic. Although it has proven beneficial to sequence analysis, machine lear…
View article: Compression and<i>k</i>-mer based Approach For Anticancer Peptide Analysis
Compression and<i>k</i>-mer based Approach For Anticancer Peptide Analysis Open
Our research delves into the imperative realm of anti-cancer peptide sequence analysis, an essential domain for biological researchers. Presently, neural network-based methodologies, while exhibiting precision, encounter challenges with a …
View article: DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images
DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images Open
Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key role in recognizing antigens, including those associated with cancer. Recent advancements in …
View article: Nearest Neighbor CCP-Based Molecular Sequence Analysis
Nearest Neighbor CCP-Based Molecular Sequence Analysis Open
Molecular sequence analysis is crucial for comprehending several biological processes, including protein-protein interactions, functional annotation, and disease classification. The large number of sequences and the inherently complicated …
View article: DeepPWM-BindingNet: Unleashing Binding Prediction with Combined Sequence and PWM Features
DeepPWM-BindingNet: Unleashing Binding Prediction with Combined Sequence and PWM Features Open
A crucial challenge in molecular biology is the prediction of DNA-protein binding interactions, which has applications in the study of gene regulation and genome functionality. In this paper, we present a novel deep-learning framework to p…
View article: Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting Open
This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular …
View article: A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis
A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis Open
In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite thei…
View article: A Memetic Algorithm To Find a Hamiltonian Cycle in a Hamiltonian Graph
A Memetic Algorithm To Find a Hamiltonian Cycle in a Hamiltonian Graph Open
We present a memetic algorithm (\maa) approach for finding a Hamiltonian cycle in a Hamiltonian graph. The \ma is based on a proven approach to the Asymmetric Travelling Salesman Problem (\atspp) that, in this contribution, is boosted by t…
View article: When Protein Structure Embedding Meets Large Language Models
When Protein Structure Embedding Meets Large Language Models Open
Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machin…
View article: Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences
Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences Open
Data visualization plays a crucial role in gaining insights from high-dimensional datasets. ISOMAP is a popular algorithm that maps high-dimensional data into a lower-dimensional space while preserving the underlying geometric structure. H…
View article: Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information Open
Effective representation of data is crucial in various machine learning tasks, as it captures the underlying structure and context of the data. Embeddings have emerged as a powerful technique for data representation, but evaluating their q…
View article: Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification
Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification Open
Biological sequence classification is vital in various fields, such as genomics and bioinformatics. The advancement and reduced cost of genomic sequencing have brought the attention of researchers for protein and nucleotide sequence classi…
View article: Efficient Sequence Embedding For SARS-CoV-2 Variants Classification
Efficient Sequence Embedding For SARS-CoV-2 Variants Classification Open
Kernel-based methods, such as Support Vector Machines (SVM), have demonstrated their utility in various machine learning (ML) tasks, including sequence classification. However, these methods face two primary challenges:(i) the computationa…
View article: Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences
Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences Open
In the midst of the global COVID-19 pandemic, a wealth of data has become available to researchers, presenting a unique opportunity to investigate the behavior of the virus. This research aims to facilitate the design of efficient vaccinat…