What is SIEVE?¶ SIEVE (Sparse Interpretable Exome Variant Explainer) is a deep learning framework for discovering disease-associated genetic variants from exome sequencing data in case-control studies. What Makes SIEVE Different?¶ Unlike existing methods: - Direct VCF Processing: No conversion to PLINK or custom formats required - Annotation-Ablation Protocol: Quantifies how much of the ranking is carried by genome structure and how much by supplied annotation - Position-Aware: Learns spatial relationships between variants - Built-in Interpretability: Embedding sparsity regularisation incorporated into training, and variant/gene attribution via Integrated Gradients - Statistical Validation: Null baseline analysis establishes significance thresholds
DNAN is an experimental machine-learning architecture designed to learn from ordered, repeating patterns in chronological data. Instead of relying only on dense layers of abstract weights, DNAN uses a population of explicit prototype agents. Each agent stores a representative his…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…
Background and Objective: Self-harm is a psychologically damaging behavior, and its accurate differentiation from other wounds (violence, accidents, burns, diabetic ulcers) is critically important in forensic medicine. However, this differentiation often falls into a diagnostic "…
Surveillance anomaly detection systems built around a single monolithic deep network are difficult to interpret, brittle to distribution shift, and offer operators no rationale on which to act. This paper presents VisionGuard, an explainable deep learning framework that reorganiz…
The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…