Logistic Regression-based Model Research Articles

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

IntroductionNeonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.ObjectiveOur objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children’s National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors.Study designOur study design optimized eight ML algorithms – Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network – to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score.ResultsThe results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model’s performance is comparable to other common ML models.ConclusionLogistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods.

Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non-hormonal peptide sequences. Initially, we developed similarity-based methods utilizing BLAST and MERCI software. Although these similarity-based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning-based models. Our logistic regression-based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity-based and machine learning-based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web-based server called HOPPred. This server offers a unique feature that allows the identification of hormone-associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

Biomarkers of Alzheimer's Disease Associated with Programmed Cell Death Reveal Four Repurposed Drugs.

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia. Programmed cell death (PCD) is mainly characterized by unique morphological features and energy-dependent biochemical processes. The predominant pathway leading to cell death in AD has not been thoroughly analyzed, although there is evidence of neuron loss in AD and numerous pathways of PCD have been associated with this process. A better understanding of the systems biology underlying the relationship between AD and PCD could lead to the development of new therapeutic approaches. To this end, publicly available transcriptome data were examined using bioinformatic methods such as differential gene expression and weighted gene coexpression network analysis (WGCNA) to find PCD-related AD biomarkers. The diagnostic significance of these biomarkers was evaluated using a logistic regression-based predictive model. Using these biomarkers, a multifactorial regulatory network was developed. Last, a drug repositioning study was conducted to propose new drugs for the treatment of AD targeting PCD. The development of 3PM (predictive, preventive, and personalized) drugs for the treatment of AD would be enabled by additional research on the effects of these drugs on this disease.

Intention to Act: Predicting Bystander Intervention in Violent Situations in South Korea.

The purpose of this study is to explore the predictors of bystanders' intentions to intervene and types of intended intervention in domestic violence (DV) and sexual violence (SV) situations in South Korea. Using nationally representative data from the 2016 National Fact-Finding Survey on Gender Equality (N = 7,399) and logistic regression-based models, this study focused on two key predictors, bystander gender and attitudes about gender equality, while controlling for age, educational level, and employment status. Our findings indicated that males and females were equally likely to state intentions to intervene in both DV and SV situations. While both males and females were more likely to state that they would intervene indirectly rather than directly, females were even less likely to intervene directly than were males for both situations. Attitudes indicative of gender equality further increased the intention to intervene for DV and SV situations in multivariate models. Gender equality attitudes, however, decreased the intentions to intervene directly in DV but not SV situations. Limitations and implications are discussed.

Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores.

Theoretical guarantees for causal inference using propensity scores are partially based on the scores behaving like conditional probabilities. However, scores between zero and one do not necessarily behave like probabilities, especially when output by flexible statistical estimators. We perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established postprocessing method to calibrate the propensity scores. We observe that postcalibration reduces the error in effect estimation and that larger improvements in calibration result in larger improvements in effect estimation. Specifically, we find that expressive tree-based estimators, which are often less calibrated than logistic regression-based models initially, tend to show larger improvements relative to logistic regression-based models. Given the improvement in effect estimation and that postcalibration is computationally cheap, we recommend its adoption when modeling propensity scores with expressive models.

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study.

The societal burden of cognitive impairment in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear what types of risk factors best predict future cognitive impairment, if known risk factors make equally accurate predictions across different socioeconomic groups, and if existing prediction models are equally accurate across different subpopulations. This paper aimed to identify which domain of health information best predicts future cognitive impairment among Chinese older adults and to examine if discrepancies exist in predictive ability across different population subsets. Using data from the Chinese Longitudinal Healthy Longevity Survey, we quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors and hobbies, psychological factors, diet, exercise and sleep, chronic diseases, and 3 recently published logistic regression-based prediction models to predict 3-year risk of cognitive impairment in the general Chinese population and among male, female, rural-dwelling, urban-dwelling, educated, and not formally educated older adults. Predictive ability was quantified using the area under the receiver operating characteristic curve (AUC) and sensitivity-specificity curves through 20 repeats of 10-fold cross-validation. A total of 4047 participants were included in the study, of which 337 (8.3%) developed cognitive impairment 3 years after baseline data collection. The risk factor groups with the best predictive ability in the general population were demographics (AUC 0.78, 95% CI 0.77-0.78), cognitive tests (AUC 0.72, 95% CI 0.72-0.73), and instrumental activities of daily living (AUC 0.71, 95% CI 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all 3 recreated prediction models had significantly higher AUCs when making predictions among female older adults compared to male older adults and among older adults with no formal education compared to those with some education. This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among Chinese older adults. However, the most predictive risk factors and existing models have lower predictive power among male, urban-dwelling, and educated older adults. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.

Evidence for identification of acute myeloid leukemia using peripheral blood's infrared spectrum and logistic regression

Invasive damage, poor prognosis and survival are problems for the traditional diagnostic and therapeutic methods of acute myeloid leukemia (AML), especially for the middle-aged and elderly. High sensitivity, less pain and fast speed are demands of AML diagnosis or long-term regular monitoring. Infrared (IR) spectroscopy has the potential to compensate for existing deficiencies and is explored in our work. Spectral differences between AML patients and controls are observed on the IR spectra, which indicate the changes in lipids and proteins in patients. The peak shift at 1390 cm−1 occurs on both peripheral blood (PB) and bone marrow (BM) spectra of AML patients making it possible to use PB rather than BM as the diagnostic source, which can relieve the suffering of diagnosis and monitoring. The logistic regression-based classification model can perfectly identify AML patients and controls with an accuracy of 100 % and the area under curve is equal to 1.00. Besides, some markers for identifying AML patients and controls are found. Statistical analysis shows that the peak area ratio A1451/A1390 has a significant difference, and the decision tree-based classification model finds that on the first derivative spectrum, the position of the largest difference is at 1391 cm−1. Furthermore, the proposed criterion based on markers we found can distinguish AML patients and controls with high accuracy of 95 %. Results show that PB’s IR spectrum combined with classification models can monitor the molecular-level biochemical changes and accurately identify AML patients and controls, which is helpful for the effective and efficient diagnosis and prognosis of AML.

Analysis of treatment response based on 1.5T magnetic resonance imaging texture analysis in stereotactic body radiotherapy of hepatocellular carcinoma

PurposeMagnetic Resonance Image (MRI)-guided stereotactic body radiotherapy (MRgSBRT) is a promising technique in the treatment of hepatocellular carcinoma (HCC). However, treatment response varies among patients. The aim of this study was to evaluate the treatment response using radiomics features extracted from 1.5 T MRI in HCC patients treated with SBRT. Materials and methods19 patients with biopsy-proven HCC who were treated with SBRT by 1.5 T MRI-guided were enrolled, all of whom were treated with 8–10 fractions with biological effective dose (BED) range of 95.2–107.1 Gy. We acquired images of pretreatment, delivered BEDs of 35–40 Gy, and delivered BEDs of 55–60 Gy. We combined three classic feature selection methods: least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and random forest (RF) to extract the intersection of radiomics features from the gross tumor volume (GTV) and features were averaged over the three fractions. We used the receiver operating characteristic (ROC) area under curve (AUC) obtained using leave-one-out cross-validation (LOOCV) to assess the predictive capacity. ResultsSeven patients showed response to treatment based on post-treatment imaging studies. The optimum intersection of radiomics features selected by three methods was Neighborhood Grey Tone Difference Matrix (NGTDM) contrast and NGTDM strength. The logistic regression-based model achieved an AUC of 0.821 (95% confidence interval, 0.618–1). ConclusionsRadiomics features containing biological prognostic information extracted during MRgSBRT could predict tumor treatment response, facilitating stratification of high-risk patients and providing clinical application value for individualized care of patients with different response to treatment.

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model

BackgroundEarly identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. ObjectiveWe aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. MethodsA supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. ResultsUpon internal validation, the machine learning and logistic regression model’s area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). ConclusionsIn this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model.

Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

BMP4, SGSH, and SLC11A2 are Predicted to Be Biomarkers of Aging Associated with Programmed Cell Death.

Most neurodegenerative diseases are exacerbated by aging, with symptoms often worsening over time. Programmed cell death (PCD) is a controlled cell suicide mechanism that is essential for the stability, growth, and homeostasis of organisms. Understanding the effects of aging at the level of systems biology could lead to new therapeutic approaches for a broad spectrum of neurodegenerative diseases. In the absence of comprehensive functional studies on the relationship between PCD and aging of the prefrontal cortex, this study provides prefrontal brain biomarkers of aging associated with PCD that could open the way for improved therapeutic techniques for age-related neurodegenerative diseases. To this end, publicly available transcriptome data were subjected to bioinformatic analyses such as differential gene expression, functional enrichment, and the weighted gene coexpression network analysis (WGCNA). The diagnostic utility of the biomarkers was tested using a logistic regression-based prediction model. Three genes, namely BMP4, SGSH, and SLC11A2, were found to be aging biomarkers associated with PCD. Finally, a multifactorial regulatory network with interacting proteins, transcription factors (TFs), competing endogenous RNAs (ceRNAs), and microRNAs (miRNAs) was constructed around these biomarkers. The elements of this multifactorial regulatory network were mainly enriched in BMP signaling. Further exploration of these three biomarkers and their regulatory elements would enable the development of 3PM (predictive, preventive, and personalized) medicine for the treatment of age-related neurodegenerative diseases.

Machine learning-based prediction of pregnancy outcomes in couples with non-obstructive azoospermia using micro-TESE for ICSI: a retrospective cohort study

Objective: To develop a clinically applicable tool for predicting clinical pregnancy, providing individualized patient counseling, and helping couples with non-obstructive azoospermia (NOA) decide whether to use fresh or cryopreserved spermatozoa for oocyte insemination before microdissection testicular sperm extraction (mTESE). Methods: A total of 240 couples with NOA who underwent mTESE-ICSI were divided into two groups based on the type of spermatozoa used for intracytoplasmic sperm injection (ICSI): the fresh and cryopreserved groups. After evaluating several machine learning algorithms, logistic regression was selected. Using LASSO regression and 10-fold cross-validation, the factors associated with clinical pregnancy were analyzed. Results: The area under the curves (AUCs) for the fresh and cryopreserved groups in the Logistic Regression-based prediction model were 0.977 and 0.759, respectively. Compared with various modeling algorithms, Logistic Regression outperformed machine learning in both groups, with an AUC of 0.945 for the fresh group and 0.788 for the cryopreserved group. Conclusion: The model accurately predicted clinical pregnancies in NOA couples.

A logistic regression-based model for classification and evaluation of chemical composition of silicate glass products

Glass is one of the earliest human-made materials, and it has played an irreplaceable role in the development of human civilization. In ancient China, glass was an important commodity on the Silk Road, and it became a valuable witness to early trade and cultural exchanges between China and the West. Due to manufacturing processes, ancient glass is easily affected by burial environments and undergoes weathering. During the weathering process, there is a significant exchange of elements between the glass and the environment, causing changes in the proportions of the glass's chemical composition, which can affect the correct determination of the glass type. In this paper, we studied multiple sets of weathered and unweathered silicate glasses, extracted data on the chemical composition, color, and patterns of the sampling points, and first found a significant difference between the six chemical composition elements in the unweathered parts for the two silicate glass categories. Then, we introduced logistic coefficients and established a chemical composition evaluation model based on logistic regression to obtain the classification patterns of the two glass types. To further subdivide the glass into subcategories, we selected the top five chemical composition contents with the highest standard deviation for clustering under each of the two glass types and divided them into three subcategories. We obtained specific partitioning methods and results and analyzed the rationality of the results.

Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data.

High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.

Estimating promotion effects in email marketing using a large-scale cross-classified Bayesian joint model for nested imbalanced data

We consider a large-scale, cross-classified nested (CRON) joint model for modeling customer responses to opening, clicking, and purchasing from promotion emails. Our logistic regression-based joint model contains crossing of promotions and customer effects and allows estimation of the heterogeneous effects of different promotion emails, after adjusting for customer preferences, attributes, and historical behaviors. Using data from an email marketing campaign of an apparel retailer, we exhibit the varying effects of promotions not only based on the contents of the email but also across the three different stages, viz. open, click, and purchase of the conversion funnel. We conduct Bayesian estimation of the parameters in the joint model by using a block Metropolis–Hastings algorithm that not only incorporates nested subsampling to tackle the severe imbalance between conversions and no conversions but also uses additive transformation-based modifications of random walk Metropolis to scale estimation for large numbers of customers. We extend our approach to a segmented cross-classified nested (SCRON) joint model that encompasses the possibility of varying promotion effects across different customer segments. The resultant high-dimensional model is estimated using spike-and-slab priors on the promotion and customer segment interactions. Our nested joint model accounts for the correlations in customer preferences across the conversion funnel. Based on the promotion estimates from the model, we demonstrate how marketers can use different priced, nonpriced, and combination of price and nonprice promotions to increase brand awareness or increase purchases. Comparing estimates from CRON and SCRON models, we display the benefits of targeted marketing by using email promotion lists which are separately optimized for the different customer segments.

Abstract P2-23-01: Patient-derived xenografts allow deconvolution of single agent and combination chemotherapy responses in triple-negative breast cancer

Abstract Background: Triple-negative breast cancer (TNBC) patients frequently receive combination chemotherapy treatment, but a direct comparison of response to carboplatin, docetaxel, and their combination in 50 TNBC patient-derived xenografts (PDXs) showed that combination treatment was largely ineffective at generating enhanced responses over the best single agent. This suggests de-escalation of chemotherapy may be possible if molecular mechanisms and biomarkers underlying response to individual treatments can be identified. To this end, we performed multi-omics profiling for the 50 TNBC PDXs. Methods: Orthotopic TNBC PDXs were treated with four weekly cycles of docetaxel, carboplatin, or the combination. Changes in tumor volume after 4 weeks of treatment were assessed quantitatively and by modified RECIST criteria. Genomic, transcriptomic, and mass-spectrometry-based proteomic profiling were performed on baseline tumors prior to treatments to identify associations with chemotherapy response at the gene and pathway level. ProMS was used to integrate both RNA and protein data to select a 5 RNA feature combination for optimized prediction of carboplatin response in a logistic regression model. Publicly available neoadjuvant chemotherapy clinical datasets with transcriptomic data and response information used for validation/testing included TNBC samples from: GSE18864, I-SPY2 (GSE194040), and BrighTNess (GSE164458). Results: Proteogenomic profiles revealed distinct genes associated with response to each agent and their combination, respectively, suggesting distinct molecular mechanisms underlying response to each treatment. A substantial number of genes associated with single agent and combination treatment were validated in multiple independent patient cohorts receiving platinum and taxane containing neoadjuvant therapy, confirming clinical relevance of our PDX panel. For the same treatment, different types of molecular data identified distinct sets of associated genes, providing highly complementary information. At the pathway level, RNA and protein data converged to metabolic and E2F/G2M related pathways which were upregulated in PDXs resistant or responsive to all treatment types, respectively, while variable levels of MYC-related proliferation pathways were observed across all treatments suggesting pathways that are common across and unique to different treatments. Several individual genes found to be higher in PDXs with better response to either single-agent had discriminatory power in external clinical TNBC datasets treated with similar neoadjuvant chemotherapy regimens. In addition, a logistic regression-based carboplatin response prediction model trained to select a group of 5 RNA markers (TKT, MAGI2, ATF6B, MCM7, LRP6) using both RNA and protein data performed the best in predicting response to cisplatin in a clinical TNBC dataset vs predicting response to other datasets with taxane and platinum + taxane combination containing chemotherapy regimens, demonstrating specificity of the prediction model. These results suggest potential individual biomarkers or biomarker combinations to select TNBC tumors that may respond to either single agent carboplatin, docetaxel, or their combination. PDXs refractory to all treatment arms had higher levels of proteostasis-related pathways including proteasome degradation and the unfolded protein response (UPR) related to endoplasmic reticulum stress and altered levels of chromatin regulation. Subsequent pharmacological targeting of the UPR pathway and targeting HDACs enhanced chemotherapy response. Conclusion: Proteogenomic characterization identifies molecular mechanisms and putative biomarkers for stratifying TNBC tumors for single or combination chemotherapy treatments, suggests targeted therapies to augment chemotherapy response, and provides a valuable resource for researchers and clinicians. Citation Format: Jonathan T. Lei, Chen Huang, Ramakrishnan R. Srinivasan, Suhas Vasaikar, Lacey E. Dobrolecki, Alaina N. Lewis, Na Zhao, Jin Cao, Susan G. Hilsenbeck, C. Kent Osborne, Mothaffar Rimawi, Matthew J. Ellis, Varduhi Petrosyan, Alexander B. Saltzman, Anna Malovannaya, John D. Landua, Bo Wen, Antrix Jain, Gerburg M. Wulf, Shunqiang Li, Daniel C. Kraushaar, Tao Wang, Xi Chen, Gloria V. Echeverria, Meenakshi Anurag, Bing Zhang, Michael T. Lewis. Patient-derived xenografts allow deconvolution of single agent and combination chemotherapy responses in triple-negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-01.

Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy

Totally 167 patients were admitted at cardiology ward in Villa Scassi hospital, Genoa, Italy. We worked with two control groups: heart failure 59 patients (mean age: 71.37 ± 13.27 years) and chronic-ischemic heart disease 108 patients (mean age: 68.85 ± 11.3 years). Nine parameters: Hb, Serum Creatinine, LDL, HDL, Triglycerides, ALT, AST, hs-cTnI, CRP were evaluated onset to hospitalization. We aimed to identify significant independent predictors relative to the outcome of heart failure versus chronic-ischemic heart disease and select combination of biochemical parameters in logistic regression-based model that would provide on average excellent discrimination to the outcome of heart failure versus chronic-ischemic heart disease in elderly population. Applying 20-fold repeated stratified cross-validation, 4:1 train/test ratio split, we have found that model: pHF=eα+β1Hb+β2SerumCreatinine+β3AST+β4hs-cTnI+β5CRP1+eα+β1Hb+β2SerumCreatinine+β3AST+β4hs-cTnI+β5CRP, pHF:probability of heart failure, provides best discrimination of the outcome of heart failure against chronic-ischemic heart disease, having learned coefficients: α,β1,β2,β3,β4,β5 upon training set.

SQL injection attack detection in network flow data

SQL injections rank in the OWASP Top 3. The literature shows that analyzing network datagrams allows for detecting or preventing such attacks. Unfortunately, such detection usually implies studying all packets flowing in a computer network. Therefore, routers in charge of routing significant traffic loads usually cannot apply the solutions proposed in the literature. This work demonstrates that detecting SQL injection attacks on flow data from lightweight protocols is possible. For this purpose, we gathered two datasets collecting flow data from several SQL injection attacks on the most popular database engines. After evaluating several machine learning-based algorithms, we get a detection rate of over 97% with a false alarm rate of less than 0.07% with a Logistic Regression-based model.

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems. To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.