Fang‐Xiang Wu

One-dimensional CNNs for Near-Infrared Prediction of Protein and Moisture in Cereal Grains: The Effects of Architecture and Input Preparation

This study compares CNNs, PLSR, SVR, and XGBoost for NIR prediction of protein and moisture in cereal grains, finding that model accuracy depends more on input preparation and sample size than network architecture.

Macronutrient Distribution and Protein Secondary Structure of Cool-Season Oats Revealed by Synchrotron-Based Mid-IR Spectroscopy and FTIR Chemical Imaging: Effects of Variety and Steam-Pressure Toasting Duration

This study uses FTIR imaging and spectroscopy to analyze protein secondary structures and macronutrient distribution in oats, showing that toasting significantly alters protein structure while variety effects are minor.

HyMM: Hybrid method for disease-gene prediction by integrating multiscale module structures

This paper introduces HyMM, a hybrid method for disease-gene prediction that integrates multiscale module structures, showing improved performance through cross-validation and independent testing.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

DeepLncLoc predicts lncRNA subcellular localization using a subsequence embedding method and text CNN, outperforming k-mer-based models and existing predictors.

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

SSRE improves cell type identification in scRNA-seq by enhancing similarity measurement through sparse subspace representation and gene selection.

Genome-wide prediction and integrative functional characterization of Alzheimer's disease-associated genes

This study predicts Alzheimer's disease-associated genes using brain gene networks and identifies causal variants, providing a resource for therapeutic target discovery.

scASK: A novel ensemble framework for classifying cell types based on single-cell RNA-seq data

scASK is an ensemble framework for classifying cell types from scRNA-seq data, achieving better accuracy and robustness than existing methods.