AIMC Topic: Computational Biology

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Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering.

IEEE/ACM transactions on computational biology and bioinformatics
Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and high dimensionality of transcriptome, remain unsolved. Here, we present a ...

Weakly-Supervised Convolutional Neural Network Architecture for Predicting Protein-DNA Binding.

IEEE/ACM transactions on computational biology and bioinformatics
Although convolutional neural networks (CNN) have outperformed conventional methods in predicting the sequence specificities of protein-DNA binding in recent years, they do not take full advantage of the intrinsic weakly-supervised information of DNA...

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a ...

Representational fluidity in embodied (artificial) cognition.

Bio Systems
Theories of embodied cognition agree that the body plays some role in human cognition, but disagree on the precise nature of this role. While it is (together with the environment) fundamentally engrained in the so-called 4E (or multi-E) cognition sta...

Drug Repurposing Using Deep Embeddings of Gene Expression Profiles.

Molecular pharmaceutics
Computational drug repositioning requires assessment of the functional similarities among compounds. Here, we report a new method for measuring compound functional similarity based on gene expression data. This approach takes advantage of deep neural...

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable.

Journal of biomedical informatics
OBJECTIVE: To develop and evaluate an efficient Trie structure for large-scale, rule-based clinical natural language processing (NLP), which we call n-trie.

Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method.

Mathematical biosciences
LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we prop...

Predict effective drug combination by deep belief network and ontology fingerprints.

Journal of biomedical informatics
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief netwo...

Prediction of 8-state protein secondary structures by a novel deep learning architecture.

BMC bioinformatics
BACKGROUND: Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on struct...