AIMC Topic: Machine Learning

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High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

Neural networks : the official journal of the International Neural Network Society
Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorith...

Patterns of thought: Population variation in the associations between large-scale network organisation and self-reported experiences at rest.

NeuroImage
Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whe...

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

Computers in biology and medicine
Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized...

Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management.

Journal of medical systems
The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as leg...

Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images.

Medical physics
PURPOSE: Manual contouring of gross tumor volumes (GTV) is a crucial and time-consuming process in rectum cancer radiotherapy. This study aims to develop a simple deep learning-based autosegmentation algorithm to segment rectal tumors on T2-weighted ...

Linking of single-molecule experiments with molecular dynamics simulations by machine learning.

eLife
Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide data, such as donor-acceptor distances, whereas the latter give atomistic information, ...

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

Biotechnology advances
Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating com...

Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network.

PloS one
The Morris water maze test (MWM) is a useful tool to evaluate rodents' spatial learning and memory, but the outcome is susceptible to various experimental conditions. Thigmotaxis is a commonly observed behavioral pattern which is thought to be relate...

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

PloS one
The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independ...

Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Statistical methods in medical research
Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produ...