AIMC Topic: Machine Learning

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A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

BMC systems biology
BACKGROUND: There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). Howev...

What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents.

Annals of human biology
BACKGROUND: Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Auto...

Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations.

Bioinformatics (Oxford, England)
MOTIVATION: Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populati...

LncRNA-ID: Long non-coding RNA IDentification using balanced random forests.

Bioinformatics (Oxford, England)
MOTIVATION: Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate...

A Machine Learning Method for Power Prediction on the Mobile Devices.

Journal of medical systems
Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption o...

Learning with hidden variables.

Current opinion in neurobiology
Learning and inferring features that generate sensory input is a task continuously performed by cortex. In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from natural image...

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

Journal of medical Internet research
BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the pub...

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Neural networks : the official journal of the International Neural Network Society
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE a...

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Molecular oncology
Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI5...