Pith. sign in

REVIEW 4 major objections 8 minor 32 references

Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer

T0 review · 4 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Random forest on gene-expression data separates early from late prostate cancer stage with an 83% best F1-score, the highest across seven machine learning methods and above deep learning.

desk verdict The 83% RF F1 is a best-run artifact; the mean is 70.2% and tied with XGB, so the paper's central claim does not hold. read the letter →

arxiv 2502.09686 v1 pith:J5PWTTXD submitted 2025-02-13 cs.LG

Raziehsadat Ghalamkarian , Marziehsadat Ghalamkarian , MortezaAli Ahmadi , Sayed Mohammad Ahmadi , Abolfazl Diyanat This is my paper · ORCID
classification cs.LG
keywords prostatecancerpathologicalstagingRNA-seqRandomForestfeatureselectionSMOTEdeeplearninggeneexpressionclassification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a standard machine-learning pipeline can stage prostate cancer from gene-expression data, separating early-stage (T1/T2) tumors from late-stage (T3/T4) tumors. Using RNA-sequencing profiles of prostate tumors, the authors report that Random Forest, after false-positive-rate feature selection and SMOTE balancing, reaches a best test F1-score near 83%, the highest among seven classifiers, with a mean F1 of 70.20% over 100 runs. They also find that a deep neural network reaches 71.23% accuracy when trained with Gaussian-noise augmentation and 69.86% when trained on PCA-compressed features. If these results hold, transcriptomics could complement PSA, biopsy, and imaging for staging decisions.

What carries the argument

The load-bearing mechanism is the pairing of SelectFpr feature selection with Random Forest. SelectFpr keeps genes whose univariate association with the early/late target passes a false-positive-rate threshold, reducing the 60,660-gene matrix to a smaller discriminative set; SMOTE generates synthetic minority-class samples to counter the imbalance between early and late cases; grid-search with 5-fold cross-validation selects hyperparameters. The paper attributes the 83% best F1 to this combination, contrasting it with deep-learning and PCA-based alternatives that reach lower accuracy.

What would settle it

Reconstruct the cohort from the paper's description, count the samples, and rerun the exact pipeline: SelectFpr at alpha=0.05 on the 60,660-gene matrix, SMOTE on the training split, grid-searched Random Forest with 5-fold cross-validation, and a 20% held-out test. If the total sample count is not 486, or if Random Forest's best F1 is not 83% and its mean F1 not 70.20%, the central claim as stated would not reproduce.

Watch

Extended reading notes

Core claim

The central claim is that Random Forest extracts a usable stage signal from high-dimensional RNA-seq data for prostate cancer pathological T staging. The authors frame the finding as: after filtering 60,660 genes with SelectFpr at alpha=0.05, balancing classes with SMOTE, and tuning hyperparameters by 5-fold grid-search cross-validation, Random Forest achieved the best held-out precision, recall, and F1-score of 83%, ahead of Logistic Regression (80%), XGBoost and SVM (79%); its mean F1 over 100 runs was 70.20%. They interpret this as evidence that a relatively simple ensemble, not a deep network, is the most reliable transcriptomic classifier for this task, and that feature selection is the key to making the high-dimensional matrix usable.

Load-bearing premise

The load-bearing premise is that the dataset is exactly as described: the paper says 486 tumors, but its own tables sum to 406 samples, and every accuracy and F1 figure depends on which count is real.

Editorial extensions

If this is right

  • If the claim holds, prostate cancer staging can be done from a tissue transcriptome with a small ensemble model, without deep learning or imaging-based grading, at least in a binary early-versus-late formulation.
  • Random Forest with feature selection would be the natural baseline for future transcriptomic staging studies, and deep learning would need to justify its added complexity with a clear accuracy gain.
  • The genes selected by the best Random Forest run point to cancer pathways, giving biologists a candidate list for stage-progression markers.
  • The 71.23% accuracy from noise-augmented deep learning suggests synthetic data can partly compensate for small sample sizes in high-dimensional gene-expression studies.
  • If staging predictions are reliable at the individual level, treatment intensity could be matched to aggressiveness, reducing both overtreatment of indolent tumors and undertreatment of advanced ones.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the paper's central number is the best of 100 runs (83% F1) rather than the typical run (70.20%), so a cautious reader should treat the 83% as an upper bound of reported performance, not the expected operating point.
  • Editorial inference: Section 2.1 says the cohort has 486 tumors, but Table 2 sums to 406; because every metric is computed on that cohort, the sample-size discrepancy is a load-bearing unknown that should be resolved before the numbers are used.
  • Editorial inference: a natural independent test is to run the same pipeline on other prostate cancer RNA-seq cohorts with pathological stage labels and check whether the mean F1 stays near 70% and which selected genes replicate.
  • Editorial inference: the result that augmentation helps the full-dimensional model but hurts PCA-compressed features suggests the order of noise addition and dimensionality reduction matters; testing interpolation between the two could clarify the mechanism.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The manuscript trains seven machine learning classifiers and several deep neural network configurations on TCGA prostate cancer RNA-seq data to classify tumors into early versus late pathological T stage, using SelectFpr feature selection, SMOTE, and stochastic feature augmentation. The principal reported result is that Random Forest achieves the highest test F1-score of approximately 83%, with deep learning reaching 71.23% accuracy under augmentation, alongside differential expression and GO enrichment analyses. The claims are undermined by reporting the best of 100 runs as the achieved performance and by internal inconsistencies in the reported sample size and resampling protocol.

Significance. The application is clinically relevant, and the use of a public dataset with repeated ML runs (100 per algorithm) is a strength; the tables present both mean and best metrics. However, the headline result is a selection artifact: the mean RF F1-score is 70.20%, tied with XGB, so the claimed superiority is not supported by the paper's own data. The internal sample-size contradiction further prevents reproducibility. If corrected and re-framed around mean performance with confidence bounds, the comparative evaluation could serve as a modest benchmark, but as written the central claim is not established.

major comments (4)
  1. [§3.3, Table 5] The abstract, Section 1.4, Section 3.3.1, and the conclusion cite an RF F1-score of approximately 83% as the achieved result. Table 5, however, reports that 83% is the 'best' (maximum) F1-score across 100 runs, while the 'mean' RF F1-score is 70.20%, identical to XGB's mean of 70.20%. With a test set of only 82 samples, the maximum over 100 random splits is not an unbiased estimator of expected performance and is highly sensitive to the particular split and to the SMOTE and SelectFpr procedures fit on each training fold. The paper therefore does not supply evidence that RF outperforms LR (mean 69.30%) or XGB (mean 70.20%). The authors should base the central claim on mean performance with confidence intervals, or on a pre-specified model-selection rule, and should clearly distinguish 'best-of-runs' from expected performance.
  2. [§2.1, Table 2] Section 2.1 states that the dataset contains 486 primary prostate cancer tumors, and Section 3.1 repeats this. Table 2 reports class counts of 184 (early) and 302 (late), which sum to 486, yet the Total row is 406 and the train/test split sums to 324+82=406. These numbers cannot all be correct. Since every accuracy and F1-score in Tables 4–6 and 8 depends on the actual cohort, the discrepancy is load-bearing: the reader cannot know whether the results refer to 486 or 406 samples. The authors must reconcile the data matrix size, the class counts, and the train/test split, and state exactly which samples were used in each analysis.
  3. [§3.4, Table 8] In Section 3.4 and Table 8, all deep learning accuracies (baseline 67.12%, PCA 69.86%, ICA 60.27%, augmentation 71.23%, etc.) are reported as single-point estimates. Unlike the machine learning section, where 100 repeated runs are described (Section 3.3), the deep learning section reports no number of independent runs, no standard deviation, and no confidence interval. Single-point estimates from a train/test split of 82 test samples cannot support the comparative claims in Section 3.4.5 (e.g., that augmentation 'yields the best performance'). The authors should provide repeated-trial statistics for the neural network experiments.
  4. [§2.7, §3.3.2, Fig. 2] The resampling protocol is described inconsistently. Section 2.7 states that k-fold cross-validation is used with the number of folds set to 5; Figure 2 shows 'Cross Validation 10-Fold'; and Section 3.3.2 and Table 6 report 10-fold cross-validation results. These are different protocols, and the metrics reported in Table 5 versus Table 6 (test-set performance versus cross-validation scores) are not clearly distinguished. The authors should specify the exact evaluation protocol used for each reported number and ensure Section 2.7, Figure 2, and the results sections agree.
minor comments (8)
  1. [Abstract] The abstract contains '71. 23%' with a spurious space; it should read '71.23%'.
  2. [§2.3.1] The phrase 'ANOV A F-test' is a typo; it should be 'ANOVA F-test'.
  3. [§2.5.2, §3.4.4] The augmentation noise scale is described only as 'very small'; Eqs. (8)–(10) do not give the concrete value used in the experiments, and Section 3.4.4 does not specify the noise standard deviation for the '10x' augmentation, so the approach is not reproducible.
  4. [Table 5] The row labeled 'score func f classif' appears to be a formatting artifact; the table should state which feature-selection scoring function was used for each method, and the table header should be cleaned.
  5. [§3.5] Section 3.5 says the Random Forest model 'achieved an accuracy of 83%', but the tables report F1-score, precision, and recall; accuracy is a different metric, so the GO enrichment statement should use the correct term.
  6. [§4] The conclusion says 'maximum cross-validation score of 80.38%', while Table 6 shows the best RF with feature selection as 80.36%; these numbers should be reconciled.
  7. [§2.1] The staging labels list t1a, t1b, t1c, t2, t2a, t2b, t2c as early and t3a, t3b, t4 as late; the manuscript should clarify whether stage t2 without a letter is included and whether the same grouping was used for all classifiers.
  8. [§2.7.2] The name 'Na ¨ ıve Bayes' has a formatting issue with the dieresis; it should be 'Naïve Bayes'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical classifier evaluation with no self-referential derivation; the headline best-run F1 is a statistical-reporting concern, not a circular step.

full rationale

After walking the derivation chain, no step reduces to its own inputs. The paper's pipeline is standard supervised classification: TCGA pathological T-stage labels (early vs late) are the target; RNA-seq expression values are the input; SelectFpr feature selection, SMOTE, and grid-search hyperparameter tuning are applied to training folds; and test metrics are computed on a held-out 20% split. None of the equations in Section 2 (ICA, PCA, SFA, SMOTE) defines the target result in terms of itself, and no fitted parameter is renamed as a prediction. The F1 scores in Table 5 are empirical outcomes, not quantities constructed from the reported conclusion. The abstract's 83% is the best-of-100 RF run rather than the mean F1 of 70.20%, and the sample-count inconsistency (486 tumors stated in Section 2.1 vs 406 total in Table 2) is a genuine reporting/robustness concern, but these are statistical-selection and data-quality issues, not circularity: the test labels are independent of the model-fitting procedure, and the best-run value is not an input to any model. There are no load-bearing self-citations; all references are external. Thus the paper is not circular, though its headline performance claim is fragile and should have been reported as the mean or with confidence bounds.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim depends on several chosen hyperparameters (SelectFpr alpha, number of PCA/ICA components, augmentation noise scale, grid-searched classifier hyperparameters) and on domain assumptions about label reliability and feature-selection integrity. No invented entities are introduced.

free parameters (5)
  • SelectFpr alpha = 0.05
    Significance threshold for univariate feature selection; chosen by the authors without justification, directly controls how many genes enter the models.
  • PCA components = 100
    Number of principal components retained in the deep learning pipeline; selected without a reported explained-variance or cross-validation criterion.
  • ICA components = 100
    Number of independent components retained; chosen to match PCA for comparison.
  • SFA noise scale = sigma1 = sigma2 (unspecified value)
    Gaussian augmentation noise hyperparameters in Eq. 7-10; the value is 'very small' but never specified, yet the augmentation result (71.23%) depends on it.
  • RF hyperparameters = n_estimators=50, max_depth=20, min_samples_split=10
    Best hyperparameters from grid search on the training set; the reported F1 depends on these specific values.
assumptions (5)
  • domain assumption TCGA pathological T-stage labels are accurate, and grouping T1a-T2c as early and T3a-T4 as late is a clinically meaningful binary target.
    Section 2.1 defines this grouping; the entire supervised task is built on it.
  • domain assumption TPM-normalized RNA-seq values are comparable across the tumors, with no unmodeled batch effects that dominate stage signal.
    Section 2.1 normalizes with TPM and uses the data directly; batch effects would invalidate the learned associations.
  • domain assumption SelectFpr with an ANOVA F-test is a valid feature-selection method for this data, and its application does not leak test information when nested in CV.
    Section 2.3.1 describes SelectFpr but never states whether selection is fit inside each cross-validation fold.
  • standard math ICA assumes a linear mixing model Z = AY with statistically independent sources (Eq. 1).
    Section 2.4.1 relies on this model for ICA-based feature extraction.
  • ad hoc to paper Adding Gaussian noise with a 'very small' standard deviation (Eq. 8-10) is a valid augmentation that approximates biological variability.
    Section 2.5.2 introduces the noise without justification for the chosen scale; the 71.23% DL result depends on it.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer." pith.science (2026). https://pith.science/paper/J5PWTTXD

@misc{pith2026250209686,
  author       = {Pith},
  title        = {Pith review of: Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J5PWTTXD}},
  note         = {Machine review of arXiv:2502.09686}
}
read the original abstract

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA) testing, and biopsies underscore the critical importance of accurate staging detection in enhancing treatment outcomes and improving patient prognosis. This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The performance of the study is measured with respect to the F1-score, as well as precision and recall, all of which are calculated as weighted averages. The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm, followed by Logistic Regression at 80%, while both Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore, deep learning models with data augmentation achieved an accuracy of 71. 23%, while PCA-based dimensionality reduction reached an accuracy of 69.86%. This research highlights the potential of AI-driven approaches in clinical oncology, paving the way for more reliable diagnostic tools that can ultimately improve patient outcomes.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 31 canonical work pages

  1. [1]

    Machine learning and its applications: A review

    Sheena Angra and Sachin Ahuja. Machine learning and its applications: A review. In 2017 international conference on big data analytics and computational intelligence (ICBDAC), pages 57–60. IEEE, 2017

    work page 2017

  2. [2]

    A study on comparative analysis of feature selec- tion algorithms for students grades prediction

    Muhammad Arham Tariq. A study on comparative analysis of feature selec- tion algorithms for students grades prediction. Journal of Information and Organizational Sciences, 48(1):133–147, 2024

    work page 2024

  3. [3]

    Cancer survival by stage at diagnosis for eng- land (experimental statistics): adults diagnosed 2012, 2013 and 2014 and followed up to 2015

    Neil Bannister and John Broggio. Cancer survival by stage at diagnosis for eng- land (experimental statistics): adults diagnosed 2012, 2013 and 2014 and followed up to 2015. Produced in collaboration with Public Health England , 2016

    work page 2012

  4. [4]

    Machine learning analysis of rna-seq data for diagnostic and prognostic prediction of colon cancer

    Erkan Bostanci, Engin Kocak, Metehan Unal, Mehmet Serdar Guzel, Koray Acici, and Tunc Asuroglu. Machine learning analysis of rna-seq data for diagnostic and prognostic prediction of colon cancer. Sensors, 23(6):3080, 2023

    work page 2023

  5. [5]

    Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries

    Freddie Bray, Mathieu Laversanne, Hyuna Sung, Jacques Ferlay, Rebecca L Siegel, Isabelle Soerjomataram, and Ahmedin Jemal. Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians , 74(3):229–263, 2024

    work page 2022

  6. [6]

    Deep learning with Python

    Francois Chollet. Deep learning with Python . Simon and Schuster, 2021

    work page 2021

  7. [7]

    Nearest neighbor pattern classification

    Thomas Cover and Peter Hart. Nearest neighbor pattern classification. IEEE transactions on information theory , 13(1):21–27, 1967

    work page 1967

  8. [8]

    Advances and trends in omics technology development

    Xiaofeng Dai and Li Shen. Advances and trends in omics technology development. Frontiers in Medicine, 9:911861, 2022

    work page 2022

Show all 32 references
  1. [9]

    A deep learning approach for cancer detection and relevant gene identification

    Padideh Danaee, Reza Ghaeini, and David A Hendrix. A deep learning approach for cancer detection and relevant gene identification. InPacific symposium on bio- computing. pacific symposium on biocomputing, volume 22, page 219. NIH Public 24 Access, 2016

    2016

  2. [10]

    Research methods for cyber security

    Thomas W Edgar and David O Manz. Research methods for cyber security . Syngress, 2017

    2017

  3. [11]

    Epidemiology and prevention of prostate cancer

    Giorgio Gandaglia, Riccardo Leni, Freddie Bray, Neil Fleshner, Stephen J Freed- land, Adam Kibel, P¨ ar Stattin, Hendrick Van Poppel, and Carlo La Vecchia. Epidemiology and prevention of prostate cancer. European urology oncology , 4(6):877–892, 2021

    2021

  4. [12]

    Shinygo: a graphical gene-set enrichment tool for animals and plants

    Steven Xijin Ge, Dongmin Jung, and Runan Yao. Shinygo: a graphical gene-set enrichment tool for animals and plants. Bioinformatics, 36(8):2628–2629, 2020

    2020

  5. [13]

    Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

    Osama Hamzeh, Abedalrhman Alkhateeb, Julia Zheng, Srinath Kandalam, and Luis Rueda. Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data. BMC bioinformatics , 21:1–10, 2020

    2020

  6. [14]

    Applications of transrectal ultrasound in prostate cancer

    CJ Harvey, J Pilcher, Jonathan Richenberg, Uday Patel, and Ferdinand Frauscher. Applications of transrectal ultrasound in prostate cancer. The British journal of radiology , 85(special issue 1):S3–S17, 2012

    2012

  7. [15]

    Application of data mining technique for diagnosis of posterior uveal melanoma

    Darius Jegeleviˇ cius, Ar¯ unas Lukoˇ seviˇ cius, Alvydas Paunksnis, and Valerijus Barzdˇ ziukas. Application of data mining technique for diagnosis of posterior uveal melanoma. Informatica, 13(4):455–464, 2002

    2002

  8. [16]

    Significance of the palpable prostatic nodule

    Hugh J Jewett. Significance of the palpable prostatic nodule. Journal of the American Medical Association, 160(10):838–839, 1956

    1956

  9. [17]

    Artificial intelligence in healthcare: past, present and future

    Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology , 2(4), 2017

    2017

  10. [18]

    Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

    Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, and Ibrahim A Hameed. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews. Egyptian Informatics Journal , 22(4):455–471, 2021

    2021

  11. [19]

    Diagset: a dataset for prostate cancer histopatho- logical image classification

    Micha l Koziarski, Bogus law Cyganek, Przemys law Niedziela, Bogus law Olborski, Zbigniew Antosz, Marcin ˙Zydak, Bogdan Kwolek, Pawe l Wasowicz, Andrzej Buka la, Jakub Swad´ zba, et al. Diagset: a dataset for prostate cancer histopatho- logical image classification. Scientific...

    2024

  12. [20]

    Deep learning

    Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436–444, 2015

    2015

  13. [21]

    Bootstrap aggregating and random forest

    Tae-Hwy Lee, Aman Ullah, and Ran Wang. Bootstrap aggregating and random forest. Macroeconomic forecasting in the era of big data: Theory and practice , 25 pages 389–429, 2020

    2020

  14. [22]

    Principal components analysis (pca)

    Andrzej Ma´ ckiewicz and Waldemar Ratajczak. Principal components analysis (pca). Computers & Geosciences , 19(3):303–342, 1993

    1993

  15. [23]

    Deep learning-based artificial intelligence for prostate cancer detection at biparametric mri

    Sherif Mehralivand, Dong Yang, Stephanie A Harmon, Daguang Xu, Ziyue Xu, Holger Roth, Samira Masoudi, Deepak Kesani, Nathan Lay, Maria J Merino, et al. Deep learning-based artificial intelligence for prostate cancer detection at biparametric mri. Abdominal Radiology, 47(4):142...

    2022

  16. [24]

    Accelerating the xgboost algorithm using gpu computing

    Rory Mitchell and Eibe Frank. Accelerating the xgboost algorithm using gpu computing. PeerJ Computer Science , 3:e127, 2017

    2017

  17. [25]

    The use of prostate specific antigen, clinical stage and gleason score to predict pathological stage in men with localized prostate cancer

    Alan W Partin, John Yoo, H Ballentine Carter, Jay D Pearson, Daniel W Chan, Jonathan I Epstein, and Patrick C Walsh. The use of prostate specific antigen, clinical stage and gleason score to predict pathological stage in men with localized prostate cancer. The Journal of urolo...

    1993

  18. [26]

    Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers

    Olivier Regnier-Coudert, John McCall, Robert Lothian, Thomas Lam, Sam McClinton, and James N’Dow. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. Artificial intelligence in medicine , 55(1):25–35, 2012

    2012

  19. [27]

    Histological grade heterogeneity in multifocal prostate cancer

    EMIEL TH RUIJTER, CHRISTINA A V AN DE KAA, JACK A SCHALKEN, FRANS M DEBRUYNE, and DIRK J RUITER. Histological grade heterogeneity in multifocal prostate cancer. biological and clinical implications. The Journal of pathology, 180(3):295–299, 1996

    1996

  20. [28]

    Identifying differentially expressed transcripts associated with prostate cancer progression using rna-seq and machine learn- ing techniques

    Siva Singireddy, Abed Alkhateeb, Iman Rezaeian, Luis Rueda, Dora Cavallo- Medved, and Lisa Porter. Identifying differentially expressed transcripts associated with prostate cancer progression using rna-seq and machine learn- ing techniques. In 2015 IEEE Conference on Computati...

    2015

  21. [29]

    Artificial intelligence and machine learning in prostate cancer patient management—current trends and future perspectives

    Octavian Sabin T˘ ataru, Mihai Dorin Vartolomei, Jens J Rassweiler, Os,an Virgil, Giuseppe Lucarelli, Francesco Porpiglia, Daniele Amparore, Matteo Manfredi, Giuseppe Carrieri, Ugo Falagario, et al. Artificial intelligence and machine learning in prostate cancer patient manage...

    2021

  22. [30]

    Multivariate geostatistical simulation of coal quality data by independent components

    AE Tercan and B Sohrabian. Multivariate geostatistical simulation of coal quality data by independent components. International Journal of Coal Geology, 112:53– 66, 2013

    2013

  23. [31]

    Machine learning and deep learn- ing in project analytics: methods, applications and research trends

    Shahadat Uddin, Sirui Yan, and Haohui Lu. Machine learning and deep learn- ing in project analytics: methods, applications and research trends. Production Planning & Control , pages 1–20, 2024. 26

    2024

  24. [32]

    The optimality of naive bayes

    Harry Zhang. The optimality of naive bayes. Aa, 1(2):3, 2004. 27

    2004

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.