AIMC Topic: Computational Biology

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PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Accurate prediction of TCR binding affinity to a target antigen is important for development of immunotherapy strategies. Recent computational methods were built on various deep neural networks and used the evolutionary-based distance matrix BLOSUM t...

Multi-treatment Effect Estimation from Biomedical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural network th...

Session Introduction: Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine requires a deep understanding of complex biomedical and healthcare data, which is being generated at exponential rates and increasingly made available through public biobanks, electronic medical record systems and biomedical databa...

Improving target-disease association prediction through a graph neural network with credibility information.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their cred...

Contrastive learning of protein representations with graph neural networks for structural and functional annotations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Although protein sequence data is growing at an ever-increasing rate, the protein universe is still sparsely annotated with functional and structural annotations. Computational approaches have become efficient solutions to infer annotations for unlab...

DockNet: high-throughput protein-protein interface contact prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally...

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all ...

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools.

Methods in molecular biology (Clifton, N.J.)
Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseas...

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Methods in molecular biology (Clifton, N.J.)
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have gre...