AIMC Topic: Machine Learning

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Predicting Type III Effector Proteins Using the Effectidor Web Server.

Methods in molecular biology (Clifton, N.J.)
Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically...

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.

Methods in molecular biology (Clifton, N.J.)
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are...

Deep Mining from Omics Data.

Methods in molecular biology (Clifton, N.J.)
Since the advent of high-throughput omics technologies, various molecular data such as genes, transcripts, proteins, and metabolites have been made widely available to researchers. This has afforded clinicians, bioinformaticians, statisticians, and d...

Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.

Methods in molecular biology (Clifton, N.J.)
After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on...

Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries.

Methods in molecular biology (Clifton, N.J.)
Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to fun...

Genome-Enabled Prediction Methods Based on Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based pr...

A method for machine learning generation of realistic synthetic datasets for validating healthcare applications.

Health informatics journal
Digital health applications can improve quality and effectiveness of healthcare, by offering a number of new tools to users, which are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, nee...

In silico proof of principle of machine learning-based antibody design at unconstrained scale.

mAbs
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing ...

[Development of Clinical Pharmaceutical Services via Artificial Intelligence Adaptation].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Recently, social implementations of artificial intelligence (AI) have been rapidly advancing. Many papers have investigated the use of AI in the field of healthcare. However, there have been few studies on the adaptation of AI to clinical pharmaceuti...

Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images.

Health informatics journal
The umbilical cord is an organ that circulates oxygen and nutrition from mother to fetus during pregnancy. This study aims to classify the umbilical cord based on ultrasound images. The similarity of shape and coil between each class becomes a challe...