AIMC Topic: Machine Learning

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Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.

Biomolecules
In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type)...

Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.

PloS one
The use of image covariates to build a classification model has lots of impact in various fields, such as computer science, medicine, and so on. The aim of this paper is to develop an estimation method for logistic regression model with image covaria...

New machine learning method for image-based diagnosis of COVID-19.

PloS one
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a ...

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing ...

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows.

Proteomics
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open ...

Learning explicitly transferable representations for domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Domain adaptation tackles the problem where the training source domain and the test target domain have distinctive data distributions, and therefore improves the generalization ability of deep models. The very popular mechanism of domain adaptation i...

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.

Acta neurochirurgica
BACKGROUND: For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a...

Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.

Analytical chemistry
The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, c...

A machine learning-based prognostic predictor for stage III colon cancer.

Scientific reports
Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis ...

Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.

Scandinavian journal of trauma, resuscitation and emergency medicine
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...