DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

Improving Protein Fold Recognition by Deep Learning Networks

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and...

A machine learning information retrieval approach to protein fold recognition

MOTIVATION Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical ...

Deep learning-based CAD systems for mammography: A review article

Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable an...

Protein Fold Recognition Using Adaboost Learning Strategy

Protein fold recognition by prediction-based threading.

In fold recognition by threading one takes the amino acid sequence of a protein and evaluates how well it fits into one of the known three-dimensional (3D) protein structures. The quality of sequence-structure fit is typically evaluated using inter-residue potentials of mean force or other statistical parameters. Here, we present an alternative approach to evaluating sequence-structure fitness....