AIMC Topic: Machine Learning

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Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological syst...

A Method for Generating Synthetic Electronic Medical Record Text.

IEEE/ACM transactions on computational biology and bioinformatics
Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data, of which the most common type is the massive free-tex...

CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression.

IEEE/ACM transactions on computational biology and bioinformatics
Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgra...

An Ontology-Independent Representation Learning for Similar Disease Detection Based on Multi-Layer Similarity Network.

IEEE/ACM transactions on computational biology and bioinformatics
To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either asso...

Protein2Vec: Aligning Multiple PPI Networks with Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different sp...

Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy.

IEEE/ACM transactions on computational biology and bioinformatics
Cross-species or Cross-platform data classification is a challenging problem in the field of bioinformatics, which aims to classify data samples in one species/platform by using labeled data samples in another species/platform. Traditional classifica...

Development and Validation of a Prediction Rule for Growth Hormone Deficiency Without Need for Pharmacological Stimulation Tests in Children With Risk Factors.

Frontiers in endocrinology
INTRODUCTION: Practice guidelines cannot recommend establishing a diagnosis of growth hormone deficiency (GHD) without performing growth hormone stimulation tests (GHST) in children with risk factors, due to the lack of sufficient evidence.

International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and one-year mortality of patients with metastatic cance...

Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help des...

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Journal of psychiatric research
Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from uns...