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Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data
Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal
Gene-expression classifiers of cancer type are usually developed and validated within one cohort using random splits. Starting from a quality-aware hybrid Boruta selector from earl…
Tree Depth and Sample-Quality-Associated Features: Two Hidden Factors in Boruta Selection for RNA-Seq Data Classification
Tareque Mohmud Chowdhury, Abu Raihan Mostofa Kamal
Random Forest--based selectors such as Boruta depend on tree depth, yet Boruta's most used implementation, BorutaPy, recommends depths of 3 to 7 without published evidence. We test…
Routing Is Not Enough: Diagnosing Intra-Adapter Subspace Contention in MoE+LoRA Fine-Tuning
Mehreen Hossain Chowdhury, Nowshin Mahjabin, Ahmed Shafin Ruhan +3
Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates domain-specific updates. We test this assumption in MoE+LoRA using Python…
FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning
Md Rafid Haque, Abu Raihan Mostofa Kamal, Md. Azam Hossain
Federated Learning (FL) offers a paradigm for privacy-preserving collaborative AI, but its decentralized nature creates significant vulnerabilities to model poisoning attacks. Whil…
A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier
Tareque Mohmud Chowdhury, Farzana Tabassum, Sabrina Islam +1
Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample…