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Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

Machines: Problem Solving I

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine learning empowers phosphoproteome prediction in cancers.

Hongyang Li¹, Yuanfang Guan¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

Bioinformatics (Oxford, England)

|August 15, 2019

Summary

We developed a computational method to predict cancer phosphoproteomic profiles from proteomic, transcriptomic, and genomic data. This algorithm ranked first in a challenge for predicting phosphorylation levels in breast and ovarian cancers.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Proteomics and Bioinformatics
Cancer Research
Systems Biology

Background:

Reversible protein phosphorylation is crucial for cellular signaling and cancer progression.
Mass spectrometry-based phosphoproteomics is powerful but resource-intensive.
In silico prediction offers an alternative for analyzing cancer phosphoproteomes.

Purpose of the Study:

To develop and validate a computational algorithm for predicting phosphoproteomic profiles in cancer patients.
To provide a robust and generalizable method for understanding cancer signaling pathways.

Main Methods:

Integration of four key components: protein-phosphoprotein correlations, protein-protein interactions, cross-tissue regulatory information, and multi-site phosphorylation associations.
Development of a winning algorithm for the 2017 NCI-CPTAC DREAM Proteogenomics Challenge.
Validation on a large dataset of 108 breast and 62 ovarian cancer samples.

Main Results:

The algorithm achieved the top rank in predicting phosphorylation levels for both breast and ovarian cancer datasets.
Demonstrated robustness and generalization ability across different cancer types.
The method successfully predicted phosphoproteomic profiles from integrated omics data.

Conclusions:

The developed algorithm provides an efficient and accurate in silico approach for phosphoproteome prediction in cancer.
This method can aid in understanding cancer regulatory mechanisms and identifying therapeutic targets.
The code and results are publicly available for reproducibility and further research.