AIMC Topic: Machine Learning

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A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.

IEEE transactions on medical imaging
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and ...

Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates.

Molecular & cellular proteomics : MCP
Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is...

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Computers in biology and medicine
Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this pap...

GuideScan software for improved single and paired CRISPR guide RNA design.

Nature biotechnology
We present GuideScan software for the design of CRISPR guide RNA libraries that can be used to edit coding and noncoding genomic regions. GuideScan produces high-density sets of guide RNAs (gRNAs) for single- and paired-gRNA genome-wide screens. We a...

Prediction of potent shRNAs with a sequential classification algorithm.

Nature biotechnology
We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a give...

Active learning for computational chemogenomics.

Future medicinal chemistry
AIM: Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of protei...

Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks.

Frontiers in neural circuits
To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunn...

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

General hospital psychiatry
OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integratio...

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

Journal of neuroscience methods
BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can trans...