AIMC Topic: Computational Biology

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Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches.

Journal of integrative bioinformatics
The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand...

Prediction of protein self-interactions using stacked long short-term memory from protein sequences information.

BMC systems biology
BACKGROUND: Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental method...

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network.

Journal of chemical information and modeling
The CRISPR-Cas9 system derived from adaptive immunity in bacteria and archaea has been developed into a powerful tool for genome engineering with wide-ranging applications. Optimizing single-guide RNA (sgRNA) design to improve efficiency of target cl...

Automatic gene annotation using GO terms from cellular component domain.

BMC medical informatics and decision making
BACKGROUND: The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Bio...

Deep convolutional networks do not classify based on global object shape.

PLoS computational biology
Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for reco...

Phenotyping through Semi-Supervised Tensor Factorization (PSST).

AMIA ... Annual Symposium proceedings. AMIA Symposium
A computational phenotype is a set of clinically relevant and interesting characteristics that describe patients with a given condition. Various machine learning methods have been proposed to derive phenotypes in an automatic, high-throughput manner....

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Proteins
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using...

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets.

PLoS computational biology
Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the m...