AIMC Topic: Machine Learning

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Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

Computational and mathematical methods in medicine
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. T...

Help seeking behavior by women experiencing intimate partner violence in india: A machine learning approach to identifying risk factors.

PloS one
BACKGROUND: Despite the low prevalence of help-seeking behavior among victims of intimate partner violence (IPV) in India, quantitative evidence on risk factors, is limited. We use a previously validated exploratory approach, to examine correlates of...

An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches.

IEEE/ACM transactions on computational biology and bioinformatics
It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites...

Graph Convolutional Networks for Drug Response Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent th...

Representation Learning for the Clustering of Multi-Omics Data.

IEEE/ACM transactions on computational biology and bioinformatics
The integration of several sources of data for the identification of subtypes of diseases has gained attention over the past few years. The heterogeneity and the high dimensions of the data sets calls for an adequate representation of the data. We su...

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data.

IEEE/ACM transactions on computational biology and bioinformatics
Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this sce...

Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Since protein 3D structure prediction is very important for biochemical study and drug design, researchers have developed many machine learning algorithms to predict protein 3D structures using the sequence information only. Understanding the sequenc...

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potentia...

AMP: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel a...

Convolutional Embedded Networks for Population Scale Clustering and Bio-Ancestry Inferencing.

IEEE/ACM transactions on computational biology and bioinformatics
The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techni...