AIMC Topic: Computational Biology

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Revisit of Machine Learning Supported Biological and Biomedical Studies.

Methods in molecular biology (Clifton, N.J.)
Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should h...

Machine learning and deep analytics for biocomputing: call for better explainability.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...

Annotating gene sets by mining large literature collections with protein networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining o...

Improving precision in concept normalization.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distri...

Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide ex...

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have propose...

Deep Integrative Analysis for Survival Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we d...

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the...

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important...