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PubMed2026

Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.

The fecal immunochemical test (FIT) for detecting fecal occult blood, used alone or in combination with other stool biomarkers, has been demonstrated to be effective in the context of colorectal cancer (CRC) screening programs. However, FIT yields a significant proportion of false positives leading to unnecessary colonoscopies. In this study, we have investigated whether leftover FIT stool samples could be repurposed for proteomics analysis as a triage step for patients before recommending colonoscopy. High-throughput mass spectrometry analyses on a set of 141 FIT-positive samples (50 controls with no lesion, 45 with advanced adenomas and 46 with CRC) in combination with machine learning tools were used. Results showed that with a specificity ≥90%, a large proportion of the false FIT positives could be identified thus providing an efficient strategy for reducing unnecessary colonoscopies. Furthermore, CRC cases were also precisely predicted to be true positives, thus providing an approach for prioritizing patients for colonoscopy. In conclusion, this study demonstrates the feasibility of using proteomics for analysis of leftover FIT stool samples as an intermediate step to triage patients selected for colonoscopy in CRC screening programs.

Humans↗

PubMed2025

Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study.

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions. METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions. RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway. CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

Humans↗

PubMed2003

Singularities in mixture models and upper bounds of stochastic complexity.

A learning machine which is a mixture of several distributions, for example, a gaussian mixture or a mixture of experts, has a wide range of applications. However, such a machine is a non-identifiable statistical model with a lot of singularities in the parameter space, hence its generalization property is left unknown. Recently an algebraic geometrical method has been developed which enables us to treat such learning machines mathematically. Based on this method, this paper rigorously proves that a mixture learning machine has the smaller Bayesian stochastic complexity than regular statistical models. Since the generalization error of a learning machine is equal to the increase of the stochastic complexity, the result of this paper shows that the mixture model can attain the more precise prediction than regular statistical models if Bayesian estimation is applied in statistical inference.

Bayes Theorem↗

PubMed1999

Evaluating variable selection methods for diagnosis of myocardial infarction.

This paper evaluates the variable selection performed by several machine-learning techniques on a myocardial infarction data set. The focus of this work is to determine which of 43 input variables are considered relevant for prediction of myocardial infarction. The algorithms investigated were logistic regression (with stepwise, forward, and backward selection), backpropagation for multilayer perceptrons (input relevance determination), Bayesian neural networks (automatic relevance determination), and rough sets. An independent method (self-organizing maps) was then used to evaluate and visualize the different subsets of predictor variables. Results show good agreement on some predictors, but also variability among different methods; only one variable was selected by all models.

Algorithms↗

PubMed2002

DNA splice site detection: a comparison of specific and general methods.

In an era when whole organism genomes are being routinely sequenced, the problem of gene finding has become a key issue on the road to understanding. For eukaryotic organisms a large part of locating the genes is accomplished by predicting the likely location of splice sites on a DNA strand. This problem of splice site location has been ap- proached using a number of machine learning or statistical methods tailored more or less specifically to the nature of the problem. Recently large margin classifiers and boosting methods have been found to give improvements over more traditional methods in a number of areas. Here we compare large margin classifiers (SVM and CMLS) and boosted decision trees with the three most common models used for splice site detection (WMM, WAM, and MDT). We find that the newer methods compare favorably in all cases and can yield significant improvement in some cases.

Algorithms↗

PubMedDate not supplied

Comprehensible evaluation of prognostic factors and prediction of wound healing.

We analyzed the data of a controlled clinical study of the chronic wound healing acceleration as a result of electrical stimulation. The study involved a conventional conservative treatment, sham treatment, biphasic pulsed current, and direct current electrical stimulation. Data was collected over 10 years and suffices for an analysis with machine learning methods. So far, only a limited number of studies have investigated the wound and patient attributes which affect the chronic wound healing. There is none to our knowledge to include treatment attributes. The aims of our study are to determine effects of the wound, patient and treatment attributes on the wound healing process and to propose a system for prediction of the wound healing rate. First we analyzed which wound and patient attributes play a predominant role in the wound healing process and investigated a possibility to predict the wound healing rate at the beginning of the treatment based on the initial wound, patient and treatment attributes. Later we tried to enhance the wound healing rate prediction accuracy by predicting it after a few weeks of the wound healing follow-up. Using the attribute estimation algorithms ReliefF and RReliefF we obtained a ranking of the prognostic factors which was comprehensible to experts. We used regression and classification trees to build models for prediction of the wound healing rate. The obtained results are encouraging and may form a basis for an expert system for the chronic wound healing rate prediction. If the wound healing rate is known, then the provided information can help to formulate the appropriate treatment decisions and orient resources towards individuals with poor prognosis.

Algorithms↗

PubMed2026

ALPAR: automated learning pipeline for antimicrobial resistance.

SUMMARY: The field of machine learning in antimicrobial resistance (AMR) research has experienced rapid growth, fueled by advancements in high-throughput genome sequencing and the growing capacity of computational resources. However, the complexity and lack of standardized data preparation and bioinformatic analyses present significant challenges, especially for newcomers to the domain. In response to these challenges, we introduce ALPAR (Automated Learning Pipeline for Antimicrobial Resistance), a comprehensive AMR data analysis tool covering the entire process from processing of raw genomic data to training machine learning models to interpretation of results. Our method relies on a reproducible pipeline that integrates widely used bioinformatics tools, presenting a simplified, automatic workflow specifically tailored for single-reference AMR analysis. Accepting genomic data in the form of FASTA files as input, ALPAR facilitates the generation of machine learning-ready data tables and both the training of machine learning and the execution of genome-wide association studies (GWAS) experiments. Additionally, our tool offers supplementary functionalities such as phylogeny-based analysis of the distribution of mutations, enhancing its utility for researchers. The tool has also proven its performance in competitive benchmarks, winning the 2024 CAMDA Anti-Microbial Resistance Prediction Challenge and placing third in the 2025 edition. AVAILABILITY AND IMPLEMENTATION: ALPAR is open-source and freely accessible via GitHub (https://github.com/kalininalab/ALPAR). The pipeline is fully reproducible and can be easily installed as a Conda package (https://anaconda.org/kalininalab/ALPAR).

Machine Learning↗

PubMed2026

Decoding gene regulation in plant genomes with artificial intelligence.

One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.

Artificial intelligence↗

PubMed2006

Support vector machine for discrimination of thermophilic and mesophilic proteins based on amino acid composition.

The identification of the thermostability from the amino acid sequence information would be helpful in computational screening for thermostable proteins. We have developed a method to discriminate thermophilic and mesophilic proteins based on support vector machines. Using self-consistency validation, 5-fold cross-validation and independent testing procedure with other datasets, this module achieved overall accuracy of 94.2%, 90.5% and 92.4%, respectively. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees, when evaluated using these three validation methods. The influence of protein size on prediction accuracy was also addressed.

Amino Acids↗

PubMed2000

Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties.

Every technique for toxicity prediction and for the detection of structure-activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the potential sources of errors associated with the identification of compounds, the representation of their structures, and the calculation of chemical descriptors. It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). We propose methods applicable to the routine quality control of large chemical datasets, but our main intention is to raise awareness about this topic and to open a discussion about quality assurance in predictive toxicology. The accuracy and reproducibility of toxicity data will be reported in another paper.

Data Interpretation, Statistical↗

PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗

PubMed2001

On preprocessing of protein sequences for neural network prediction of polyproline type II secondary structures.

Polyproline type II stretches are somewhat rare on proteins. The backbone of this secondary structural element folds to a triangular form instead of the normal alpha-helix with 3.6 residues per turn. It is a very challenging task to try to detect them computationally from protein sequence. Here, we have studied the preprocessing phase in particular, which is important for any machine learning method. Preprocessing included selection of relevant data from the Protein Data Bank and investigation of learnability properties. These properties show whether the material is suitable for neural network computing. The complexity of algorithms in connection with preprocessing was briefly considered. We found that feedforward perceptron neural networks were appropriate for the prediction of polyproline type II and also relatively efficient in this task. The problem is very difficult because of the great similarity of the two classes present in the classification. Nevertheless, neural networks were able to recognize and predict about 75% of secondary structures.

Algorithms↗

PubMed2026

Mitochondria related gene signature serves as prognosis prediction and risk stratification of cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CHOL) is a highly aggressive biliary malignancy with poor clinical outcomes and limited effective prognostic biomarkers. Mitochondrial dysfunction participates in multiple oncological processes of CHOL, yet the prognostic roles of mitochondria‑related genes (MRGs) remain poorly understood. This study aimed to characterize MRGs expression in CHOL and develop a molecular prognostic model for predicting patient survival and guiding clinical management. METHODS: RNA sequencing (RNA-seq) and clinical data of CHOL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE89748) databases. Differentially expressed MRGs were identified, and 10 machine learning algorithms were used to construct prognostic models. The optimal model (highest average C-index) was selected to establish a mitochondria-related risk score (MRRS), which was validated internally and externally. A nomogram integrating clinical factors and MRRS was developed, and biological mechanisms were explored via functional and immune analyses. RESULTS: A 3-MRG (MAP3K1, MRPL18, PYGB) prognostic signature was constructed, stratifying patients into high- and low-risk groups with significantly different overall survival. The model showed high predictive accuracy, with an area under the curve (AUC) up to 0.845, and MRRS was an independent prognostic factor. The signature was associated with mitochondrial pathways, and the high-risk group had distinct immune infiltration and mutation profiles. CONCLUSIONS: A validated MRG prognostic model effectively stratifies CHOL patients and has potential clinical value for prognosis prediction. Further validation in larger cohorts is needed to confirm its applicability.

Cholangiocarcinoma (CHOL)↗

PubMedDate not supplied

Collaborative filtering on a family of biological targets.

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Ligands↗

PubMedDate not supplied

Application of expert systems analysis to interpretation of fatal cases involving amitriptyline.

Part I. Expert 4: The object of this study was to investigate the applicability of commercially available expert system shells to interpretation in forensic toxicology. Amitriptyline toxicology was selected as a pilot trial. Blood and tissue concentrations of amitriptyline and nortriptyline in fatal and nonfatal amitriptyline cases from the literature and from the Registry of Human Toxicology databank were entered into the expert system shell Expert 4 (Rivers, Elsevier). The statistical evaluation routines of the shell were used to search for patterns in the data. Successive changes in the data base were made to test for the influence of the data base on the conclusions. Finally the data base was refined, based on the evaluations, to strengthen the probabilities of the conclusions. The refined database was coupled with the Expert 4 inference engine to infer unknown parameters in the cases. The results of the expert system analysis were compared to known values and published expert opinions. The ratio of amitriptyline/nortriptyline and tissue levels of nortriptyline were found to be the most significant measures for interpretation of effect and time since ingestion. Part II. Computer Induction of Rules: Blood and tissue concentrations of amitriptyline and nortriptyline in fatal and nonfatal amitriptyline cases from the literature and from the Registry of Human Toxicology databank were entered into the expert system shell BEAGLE, (Forsyth, Machine Learning Research, Ltd.). The automatic rule induction routines of the shell were used to search for patterns in the data. The program expressed these patterns as numerical predictions or Boolean logic rules. The results were compared to those obtained with the Expert 4 (Rivers, Elsevier) using the same case knowledge base.(ABSTRACT TRUNCATED AT 250 WORDS)

Amitriptyline↗

PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗

PubMed2006

More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature.

Highly accurate knockdown functional analyses based on RNA interference (RNAi) require the possible most complete hydrolysis of the targeted mRNA while avoiding the degradation of untargeted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untargeted genes. Therefore, selecting a small number of highly active siRNAs is critical for maximizing knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and transparent machine learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines (SVMs) with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility and self-hairpin features over 2200 published siRNAs. This tool reaches an accuracy of 92.3% in cross-validation experiments. We fully present the underlying biophysical signature that involves free energy, accessibility and dinucleotide characteristics. We show that while complete silencing is possible at certain structured target sites, accessibility information improves the prediction of the 90% active siRNA target sites. Fast siRNA activity predictions can be performed on our web server at http://optirna.unl.edu/.

Artificial Intelligence↗

PubMedDate not supplied

Use of an electronic nose to diagnose bacterial sinusitis.

BACKGROUND: Having previously established that an electronic nose (enose) can distinguish among bacteria samples, between cerebrospinal fluid leak and serum, and can identify patients with ventilator-associated pneumonia, we hypothesized that bacterial sinusitis could be diagnosed by sampling exhaled gas with an enose. METHODS: Using a nasal continuous positive airway pressure mask, we sampled gas exhaled through the nose of patients with sinusitis and compared them with controls. Data were first projected onto the principal components and then classified by support vector machine (SVM), a machine learning algorithm for pattern recognition. RESULTS: SVM analysis showed good discrimination using three approaches. First, 11 samples were used to create a training set that was used to predict whether individual samples from each set were a member of the control or infected sets. The enose was correct 98.4% of the time. Second, one-half of the samples from each of the same 11 control and infected groups were used to construct a training set, which was used to predict infection in the remaining samples. The enose was correct 82% of the time. Finally, 68 samples (34 positive and 34 controls) were analyzed using a leave-one-out scheme for creating training sets and testing sets. This method, designed to reflect the generalization property of the SVM classifier, scored a classification rate of 72%. CONCLUSION: Using the enose to sample nasal exhalation from patients with suspected sinusitis, we were able to predict correctly the diagnosis of sinusitis in at least 72% of the samples. The next step will be to do forward prediction using this model.

Algorithms↗