AIMC Topic: Computational Biology

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Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach.

Molecular medicine reports
The aim of the present study was to predict pathogenic genes for primary myelofibrosis (PMF) using a system‑network approach by combining protein‑protein interaction (PPI) network and gene expression data with known pathogenic genes. PMF gene express...

A deep convolutional neural network for classification of red blood cells in sickle cell anemia.

PLoS computational biology
Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological...

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BMC genomics
BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed whe...

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

Molecules (Basel, Switzerland)
With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological...

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

Journal of theoretical biology
MOTIVATION: Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that p...

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.

BMC genomics
BACKGROUND: Cell-penetrating peptides (CPPs) are short peptides (5-30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer trea...

A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.

PLoS computational biology
The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA)...

Modular representation of layered neural networks.

Neural networks : the official journal of the International Neural Network Society
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge f...

Predicting lysine glycation sites using bi-profile bayes feature extraction.

Computational biology and chemistry
Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to unders...

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

PloS one
It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple ...