AIMC Topic:
Supervised Machine Learning

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Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.

Neural computation Jun 7, 2024
In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local infor...
Neural Networks, Computer Supervised Machine Learning Nerve Net Stochastic Processes Deep Learning Models, Neurological
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Supervised learning of enhancer-promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning.

Bioinformatics (Oxford, England) Jun 3, 2024
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to le...
Chromatin Immunoprecipitation Sequencing Promoter Regions, Genetic Enhancer Elements, Genetic Humans Transcription Factors Supervised Machine Learning Genomics
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DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.

Database : the journal of biological databases and curation May 28, 2024
While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic vari...
Humans Data Mining Databases, Genetic Data Curation Supervised Machine Learning
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Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.

Briefings in bioinformatics May 23, 2024
Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually limited in many tasks, such as chemical property prediction and virtual screening. However, most existing molecular pre-t...
Computational Biology Supervised Machine Learning Algorithms
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Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

Journal of the American Medical Informatics Association : JAMIA May 20, 2024
OBJECTIVE: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. Ho...
Female Aged Humans Algorithms Parkinson Disease Supervised Machine Learning Male Middle Aged
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Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Cell systems May 15, 2024
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial tran...
Animals Gene Expression Profiling Deep Learning Single-Cell Analysis Transcriptome Humans Software In Situ Hybridization, Fluorescence Image Processing, Computer-Assisted Single Molecule Imaging Supervised Machine Learning
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Analysis of 3D pathology samples using weakly supervised AI.

Cell May 9, 2024
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistica...
X-Ray Microtomography Prostatic Neoplasms Supervised Machine Learning Humans Prognosis Deep Learning Imaging, Three-Dimensional Male
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Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Molecular biology and evolution May 3, 2024
Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele...
Neural Networks, Computer Models, Genetic Gene Frequency Genetics, Population Supervised Machine Learning Humans
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Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation.

Radiology. Artificial intelligence May 1, 2024
Purpose To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials and Methods This retrospective study used semi-supervised learning to ...
Humans Intracranial Hemorrhages Benchmarking Machine Learning Supervised Machine Learning Retrospective Studies
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[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Apr 1, 2024
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
Supervised Machine Learning Intensive Care Units Female Algorithms Support Vector Machine Prognosis Decision Trees Middle Aged Humans Male Machine Learning Shock, Septic
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