AIMC Topic: Computational Biology

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An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.

BMC bioinformatics
BACKGROUND: DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein-DNA interfaces. As experimental methods for identifying hot spots are time-consuming...

LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.

Computational and mathematical methods in medicine
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, w...

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.

PLoS computational biology
Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes...

Drug Target Identification with Machine Learning: How to Choose Negative Examples.

International journal of molecular sciences
Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target I...

PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles.

BMC bioinformatics
BACKGROUND: Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can auto...

Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism.

Computational and mathematical methods in medicine
Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and at...

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification.

Computational and mathematical methods in medicine
For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature ...

Accurate prediction of protein-ATP binding residues using position-specific frequency matrix.

Analytical biochemistry
Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially...

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Computational biology and chemistry
Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were use...