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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.

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Video

Updated: Nov 29, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Predictive article recommendation using natural language processing and machine learning to support evidence updates

Bhuvan Sharma¹, Van C Willis¹, Claudia S Huettner¹

¹IBM Watson Health, Cambridge, Massachusetts, USA.

|November 20, 2020

Summary

This summary is machine-generated.

This study introduces an augmented intelligence system to update knowledge graphs, significantly reducing literature review workload by 99%. The system effectively identifies relevant publications, aiding in evidence-based knowledge graph maintenance.

Keywords:

artificial intelligence machine learning natural language processing precision medicine

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Last Updated: Nov 29, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Bioinformatics
Artificial Intelligence
Knowledge Representation

Background:

Knowledge graphs require continuous updates with new evidence.
Manual literature review for evidence updates is time-consuming and cognitively demanding.

Purpose of the Study:

To describe an augmented intelligence approach for updating evidence in knowledge graphs.
To reduce the human cognition load associated with evidence-based knowledge graph maintenance.

Main Methods:

Utilizing machine learning study classifiers to filter new publications.
Applying natural language processing techniques including named entity recognition and semantic mapping.
Employing vector space modeling and similarity measures to identify related articles.
Incorporating subject matter expert review for final article inclusion and consensus.

Main Results:

Machine learning classifiers achieved high F-scores (0.88-0.94).
The augmented intelligence approach reduced human literature review load by 99%.
41% of system recommendations were accepted for knowledge graph updates over 12 months.

Conclusions:

An integrated search and recommendation system enhances knowledge graph evidence updates.
Augmented intelligence significantly reduces the cognitive burden on human experts.
This approach facilitates efficient and effective knowledge graph maintenance.