Frontiers

A data-driven approach for missing well-log prediction using KNN regression

In petroleum geophysics, well logs are fundamental for subsurface characterization; however, missing logs frequently occur due to tool failure, legacy data gaps, or economic constraints, limiting ...
SciELO

Obesity classification: a comparative study of machine learning models excluding weight and height data

OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity ...
Analytics India Magazine

What is cosine similarity and how is it used in machine learning?

This article explains the concept of cosine similarity and how it is used as a metric for evaluation of data points in various applications. Cosine similarity measures the angle between two vectors to ...
Scientific Research Publishing

Performance Comparison of Various Machine Learning Approaches to Identify the Best One in Predicting Heart Disease ()

1 Electronics and Communications Department, Al-Safwa High Institute of Engineering, Qalyubia, Egypt. 2 Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, ...
Frontiers

MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets

Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume ...
GitHub

CStreet is a cell state trajectory inference method for time-series single-cell RNA-seq data. It is written in Python (3.6, 3.7 or 3.8) and is available as a command line tool ...

CStreet uses time-series information to construct the k-nearest neighbors connections within and between time points. Then, CStreet calculates the connection probabilities of cell states and ...