5 citations · 12 across the 35 of their papers we have counts for
23 papers · 1 filter
Latent-Kernel Discrete Flow Maps for Few-Step Generation
Mansoor Ahmed, Yue-Tsz Fan, Hemanth Venkateswara +1
Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token i…
EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
Mansoor Ahmed, Sujin Lee, Umar Khayaz +1
Equivariant graph neural network (GNN) methods for antibody complementarity-determining region (CDR) design achieve the highest sequence recovery but suffer from severe vocabulary…
AgForce Enables Antigen-conditioned Generative Antibody Design
Mansoor Ahmed, Murray Patterson
Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they larg…
ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning
Mansoor Ahmed, Spencer VonBank, Nadeem Taj +3
Computational antibody CDR design methods condition on antigen structure to generate binding loops. Yet, the existing architectures conflate two fundamentally distinct sub-problems…
CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design
Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan +2
Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized be…
Boosting t-SNE Efficiency for Sequencing Data: Insights from Kernel Selection
Avais Jan, Prakash Chourasia, Sarwan Ali +1
Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional biological sequencing data. t-distributed Stochastic Neighbor Embedding (t-SNE) is…
Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
Sarwan Ali, Haris Mansoor, Murray Patterson
Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationship…
Converting Time Series Data to Numeric Representations Using Alphabetic Mapping and k-mer strategy
Sarwan Ali, Tamkanat E Ali, Imdad Ullah Khan +1
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 techn…
Hilbert Curve Based Molecular Sequence Analysis
Sarwan Ali, Tamkanat E Ali, Imdad Ullah Khan +1
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…
Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm
Sarwan Ali, Haris Mansoor, Prakash Chourasia +2
In molecular structure data, SMILES (Simplified Molecular Input Line Entry System) strings are used to analyze molecular structure design. Numerical feature representation of SMILE…
EPIC: Enhancing Privacy through Iterative Collaboration
Prakash Chourasia, Heramb Lonkar, Sarwan Ali +1
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. Trad…
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering
Sarwan Ali, Prakash Chourasia, Haris Mansoor +2
The t-Distributed Stochastic Neighbor Embedding (t-SNE) has emerged as a popular dimensionality reduction technique for visualizing high-dimensional data. It computes pairwise simi…
Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks
Sarwan Ali, Taslim Murad, Prakash Chourasia +4
Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to po…
DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images
Taslim Murad, Prakash Chourasia, Sarwan Ali +2
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, incl…
A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis
Sarwan Ali, Tamkanat E Ali, Prakash Chourasia +1
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 wide…
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification
Zahra Tayebi, Sarwan Ali, Prakash Chourasia +2
Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their sp…
BioSequence2Vec: Efficient Embedding Generation For Biological Sequences
Sarwan Ali, Usama Sardar, Murray Patterson +1
Representation learning is an important step in the machine learning pipeline. Given the current biological sequencing data volume, learning an explicit representation is prohibiti…
Exploring The Potential Of GANs In Biological Sequence Analysis
Taslim Murad, Sarwan Ali, Murray Patterson
Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in ide…
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning
Prakash Chourasia, Taslim Murad, Zahra Tayebi +3
This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike sequences in a distributed way, w…
Anderson Acceleration For Bioinformatics-Based Machine Learning
Sarwan Ali, Prakash Chourasia, Murray Patterson
Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimiza…
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences
Prakash Chourasia, Sarwan Ali, Murray Patterson
The t-distributed stochastic neighbor embedding (t- SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-…
Efficient Approximate Kernel Based Spike Sequence Classification
Sarwan Ali, Bikram Sahoo, Muhammad Asad Khan +3
Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Severa…
Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location
Sarwan Ali, Babatunde Bello, Zahra Tayebi +1
With the rapid spread of COVID-19 worldwide, viral genomic data is available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a uniqu…