The support vector machine algorithm together with graph kernel functions has recently been introduced to model structure-activity relationships (SAR) of molecules from their 2D structure, without the need for explicit molecular descriptor computation. We propose two extensions to this approach with the double goal to reduce the computational burden associated with the model and to enhance its predictive accuracy: description of the molecules by a Morgan index process and definition of a second-order Markov model for random walks on 2D structures. Experiments on two mutagenicity data sets validate the proposed extensions, making this approach a possible complementary alternative to other modeling strategies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/ci050039t | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study.
Menopause
January 2025
From the School of Nursing and Rehabilitation, Shandong University, Jinan, Shandong, China.
Objective: This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing severe subjective cognitive decline (SCD) during the menopause transition, along with their associated factors.
Methods: A secondary analysis was performed using cross-sectional data from 1,264 nurses undergoing the menopause transition. The data set was randomly split into training (75%) and validation sets (25%), with the Bortua algorithm employed for feature selection.
Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.
Clin Transl Sci
January 2025
Pharmacometrica, La Fouillade, France.
Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized, placebo-controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment-specific and non-specific effects. The non-specific treatment effect varies subject by subject conditional to the individual propensity to respond to placebo.
View Article and Find Full Text PDFIdentify characteristics of Vietnamese oral squamous cell carcinoma patients by machine learning on transcriptome and clinical-histopathological analysis.
J Dent Sci
December 2024
Blood Transfusion Haematology Hospital No. 2, Ho Chi Minh City, Viet Nam.
Background/purpose: Oral squamous cell carcinoma (OSCC) is notorious for its low survival rates, due to the advanced stage at which it is commonly diagnosed. To enhance early detection and improve prognostic assessments, our study harnesses the power of machine learning (ML) to dissect and interpret complex patterns within mRNA-sequencing (RNA-seq) data and clinical-histopathological features.
Materials And Methods: 206 retrospective Vietnamese OSCC formalin-fixed paraffin-embedded (FFPE) tumor samples, of which 101 were subjected to RNA-seq for classification based on gene expression.
Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach.
Risk Manag Healthc Policy
January 2025
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, 235603, Taiwan.
Purpose: As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF.
Patients And Methods: A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records.
Predicting Color Development and Texture Changes in Tomatoes Treated With Hot Water and Exposed to High-Temperature Ethylene Using Support Vector Regression.
J Texture Stud
February 2025
Department of Mathematics, Aditya University, Surampalem, Andhra Pradesh, India.
This study investigated the impacts of hot water treatment (HWT) at 50°C or 25°C for 5 min and high-temperature ethylene (HTE) exposure at varying temperatures (20°C, 30°C, or 35°C) and durations (24, 48, or 72 h) on the postharvest quality and antioxidant properties of mature green tomatoes (MG). Color changes, physicochemical characteristics, antioxidant compounds, and overall antioxidant ability were assessed. HWT increased β-carotene levels and oxygen radical absorbance capacity (ORAC) while preserving color metrics, despite later HTE exposure.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!