AIMC Topic: Computational Biology

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Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

Nature communications
The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Cl...

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.

Analytical biochemistry
An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory b...

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.

Journal of chemical information and modeling
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, fo...

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.

International journal of molecular sciences
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental m...

Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.

Journal of biomedical informatics
Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of c...

Deep Learning-Based Framework for Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

Sensors (Basel, Switzerland)
The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor r...

Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics.

Interdisciplinary sciences, computational life sciences
Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorph...

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.

PLoS computational biology
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequenc...

AntiVPP 1.0: A portable tool for prediction of antiviral peptides.

Computers in biology and medicine
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity a...