AIMC Topic: Computational Biology

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Enhanced Protein Structural Class Prediction Using Effective Feature Modeling and Ensemble of Classifiers.

IEEE/ACM transactions on computational biology and bioinformatics
Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Id...

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the tradi...

Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.

IEEE/ACM transactions on computational biology and bioinformatics
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening an...

A Deep Learning Framework for Gene Ontology Annotations With Sequence- and Network-Based Information.

IEEE/ACM transactions on computational biology and bioinformatics
Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without function...

NegStacking: Drug-Target Interaction Prediction Based on Ensemble Learning and Logistic Regression.

IEEE/ACM transactions on computational biology and bioinformatics
Drug-target interactions (DTIs) identification is an important issue of drug research, and many methods proposed to predict potential DTIs based on machine learning treat it as a binary classification problem. However, the number of known interacting...

Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles.

IEEE/ACM transactions on computational biology and bioinformatics
Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the i...

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

Computational and mathematical methods in medicine
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia.

Computational and mathematical methods in medicine
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in ...

Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage.

Computational and mathematical methods in medicine
OBJECTIVE: Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional me...