AIMC Topic: Computational Biology

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Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions.

Theranostics
MicroRNAs (miRNAs) are short non-coding RNAs that regulate expression of target messenger RNAs (mRNAs) post-transcriptionally. Understanding the precise regulatory role of miRNAs is of great interest since miRNAs have been shown to play an important ...

Stable solution to l -based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases.

BMC medical genomics
BACKGROUNDS: A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities thr...

Identifying term relations cross different gene ontology categories.

BMC bioinformatics
BACKGROUND: The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecu...

Effective computational detection of piRNAs using n-gram models and support vector machine.

BMC bioinformatics
BACKGROUND: Piwi-interacting RNAs (piRNAs) are a new class of small non-coding RNAs that are known to be associated with RNA silencing. The piRNAs play an important role in protecting the genome from invasive transposons in the germline. Recent studi...

DotAligner: identification and clustering of RNA structure motifs.

Genome biology
The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further e...

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

Molecules (Basel, Switzerland)
Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE)...

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

BMC systems biology
BACKGROUND: Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions....

Comparison, alignment, and synchronization of cell line information between CLO and EFO.

BMC bioinformatics
BACKGROUND: The Experimental Factor Ontology (EFO) is an application ontology driven by experimental variables including cell lines to organize and describe the diverse experimental variables and data resided in the EMBL-EBI resources. The Cell Line ...

Cell ontology in an age of data-driven cell classification.

BMC bioinformatics
BACKGROUND: Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessib...

Cell type discovery and representation in the era of high-content single cell phenotyping.

BMC bioinformatics
BACKGROUND: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs a...