AIMC Topic: Machine Learning

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Machine-learning approaches to classify and understand emotion states in mice.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology

Digitalization and Bioprocessing: Promises and Challenges.

Advances in biochemical engineering/biotechnology
The production of pharmaceuticals, industrial chemicals, and food ingredients from biotechnological processes is a vast and rapidly growing industry. While advances in synthetic biology and metabolic engineering have made it possible to produce thous...

Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues.

Methods in molecular biology (Clifton, N.J.)
With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in com...

Siamese Neural Networks: An Overview.

Methods in molecular biology (Clifton, N.J.)
Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correl...

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning.

Methods in molecular biology (Clifton, N.J.)
With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and c...

Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to...

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD.

Current drug discovery technologies
BACKGROUND: Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applicatio...

Lung Nodule Detection using Convolutional Neural Networks with Transfer Learning on CT Images.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Numerous methods based on Convolutional Neural Networks (CNNs) have been proposed for lung nodule dete...

Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.

Anesthesia and analgesia
BACKGROUND: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendation...

NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data.

Neuroinformatics
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and hig...