Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]
This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …
is organized as a collection of different contributions of authors who are experts on this topic …
A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation
Motivation: Fold recognition is an important step in protein structure and function prediction.
Traditional sequence comparison methods fail to identify reliable homologies with low …
Traditional sequence comparison methods fail to identify reliable homologies with low …
Supervised machine learning algorithms for protein structure classification
We explore automation of protein structural classification using supervised machine learning
methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) …
methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) …
Target fishing for chemical compounds using target-ligand activity data and ranking based methods
In recent years, the development of computational techniques that identify all the likely
targets for a given chemical compound, also termed as the problem of Target Fishing, has …
targets for a given chemical compound, also termed as the problem of Target Fishing, has …
A study of hierarchical and flat classification of proteins
Automatic classification of proteins using machine learning is an important problem that has
received significant attention in the literature. One feature of this problem is that expert …
received significant attention in the literature. One feature of this problem is that expert …
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition
Background Predicting a protein's structural class from its amino acid sequence is a
fundamental problem in computational biology. Much recent work has focused on …
fundamental problem in computational biology. Much recent work has focused on …
Scalable algorithms for string kernels with inexact matching
We present a new family of linear time algorithms based on sufficient statistics for string
comparison with mismatches under the string kernels framework. Our algorithms improve …
comparison with mismatches under the string kernels framework. Our algorithms improve …
Automatic structure classification of small proteins using random forest
Background Random forest, an ensemble based supervised machine learning algorithm, is
used to predict the SCOP structural classification for a target structure, based on the …
used to predict the SCOP structural classification for a target structure, based on the …
When Protein Structure Embedding Meets Large Language Models
Protein structure analysis is essential in various bioinformatics domains such as drug
discovery, disease diagnosis, and evolutionary studies. Within structural biology, the …
discovery, disease diagnosis, and evolutionary studies. Within structural biology, the …
From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets
Understanding protein structures is crucial for various bioinformatics research, including
drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification …
drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification …