Zobrazeno 1 - 10
of 14 319
pro vyhledávání: '"Chourasia A"'
We introduce a set of useful expressions of Differential Privacy (DP) notions in terms of the Laplace transform of the privacy loss distribution. Its bare form expression appears in several related works on analyzing DP, either as an integral or an e
Externí odkaz:
http://arxiv.org/abs/2411.09142
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 also gained po
Externí odkaz:
http://arxiv.org/abs/2411.05173
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 and processi
Externí odkaz:
http://arxiv.org/abs/2411.05167
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering
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 and random
Externí odkaz:
http://arxiv.org/abs/2410.15688
Autor:
Ali, Sarwan, Murad, Taslim, Chourasia, Prakash, Mansoor, Haris, Khan, Imdad Ullah, Chen, Pin-Yu, Patterson, Murray
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 examining how
Externí odkaz:
http://arxiv.org/abs/2410.12655
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 sequencing
Externí odkaz:
http://arxiv.org/abs/2409.06694
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 nature of p
Externí odkaz:
http://arxiv.org/abs/2409.04922
Publikováno v:
SimBig2023
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 embeddings
Externí odkaz:
http://arxiv.org/abs/2403.19844
Recently introduced magnetic materials called altermagnets (AM) feature zero net magnetization but a momentum dependent magnetic exchange field, which can have intriguing implications when combined with superconductivity. In our work, we use the quas
Externí odkaz:
http://arxiv.org/abs/2403.10456
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 their astoundin
Externí odkaz:
http://arxiv.org/abs/2402.08117