AIMC Topic: Computational Biology

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Gene Ontology Semantic Similarity Analysis Using GOSemSim.

Methods in molecular biology (Clifton, N.J.)
The GOSemSim package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among GO terms, gene products and gene clusters. In this chapter, I illus...

An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.

Current protein & peptide science
The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localizatio...

Statistical and Machine Learning Methods for eQTL Analysis.

Methods in molecular biology (Clifton, N.J.)
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population...

AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...

PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to class...

PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. His...

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large colle...