Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches

Bing Xue; Lifeng Peng; Mengjie Zhang; Samaneh Azari

REVIEW 4 major objections 5 minor 17 references

Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read A genetic-programming scoring function re-ranks candidate peptide-spectrum matches and raises correct de novo identifications from 67 to 79 spectra out of 120.

desk verdict A modest but real GP-based PSM rescoring paper; the 10% FPR claim needs better statistical backing and an explicit disjointness statement. read the letter →

arxiv 1908.08010 v1 pith:IFD7IYW6 submitted 2019-08-12 cs.NE q-bio.QM

Samaneh Azari , Bing Xue , Mengjie Zhang , Lifeng Peng This is my paper · ORCID

classification cs.NEq-bio.QM

keywords geneticprogrammingsymbolicregressionpeptide-spectrummatchdenovopeptidesequencingtandemmassspectrometryscoringfunctionfalsepositiveratePSMre-ranking

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

The paper proposes GP-PSM, a genetic-programming method that learns a scoring function for peptide-spectrum matches (PSMs) and uses it to re-rank the candidate peptides that a de novo sequencing algorithm proposes for each MS/MS spectrum. The authors aim to fix a weak point in de novo identification: the top-ranked candidate is often wrong, and existing scoring functions do not separate correct from incorrect matches well. They train a symbolic regression model on thousands of PSMs with known identities, using eleven features that describe how an experimental spectrum matches the theoretical spectrum of a candidate peptide. On an evaluation set of 120 spectra, re-ranking with the learned function raises correct identifications from 67 to 79 and lowers the false positive rate from 0.44 to 0.34, a 10-percentage-point improvement over the unprocessed de novo output. A careful reader would care because better PSM scoring directly improves confidence in peptide identifications without changing the sequencing instrument or algorithm.

What carries the argument

The central object is the GP-evolved scoring function, a tree-structured arithmetic expression over eleven PSM features: matched peak intensity, numbers of matched and unmatched peaks, precursor mass difference, consecutive b-ion and y-ion matches, cosine similarity, Euclidean distance, Hamming distance, and two SEQUEST-style fixed- and variable-length vector scores. The GP runs as symbolic regression: fitness is the relative sum of squared error between the evolved score and the known database-search confidence score, and the terminal set includes the eleven features plus constants, with protected arithmetic as the function set. The evolved tree re-scores each candidate peptide in a spectrum's output list; the highest-scoring candidate is then reported as the identification. Its work is to compress the eleven weak individual similarity signals into one ranking that separates the true peptide from near-synonymous decoys.

What would settle it

Re-apply the learned function to a fresh, independently created set of MS/MS spectra with known peptide identities and count top-ranked correct matches; if the improvement over the original de novo rankings is not around 10 percentage points, the paper's central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that a PSM scoring function evolved by genetic programming can serve as a post-processing re-ranker that outperforms the raw confidence scores of a de novo sequencing algorithm and also beats two regression baselines, Random Forest and Support Vector Regression. The learned function combines eleven spectrum-peptide similarity features into a single score, and when each spectrum's five candidate peptides are re-ranked by that score, the correct peptide reaches the top rank more often than it did under the original de novo score. Concretely, on 120 benchmark spectra the method identifies 79 spectra correctly versus 67 before re-ranking, identifies 20 of the 25 correct matches that the original tool had ranked below the top, and reduces the false positive rate from 0.44 to 0.34. The paper presents this as evidence that GP's nonlinear, feature-selecting symbolic expressions are better discriminators for full-length peptide assignments than hand-built or linear scoring functions.

Load-bearing premise

The scoring function is trained on matches produced by a database search and then applied to matches produced by a different de novo sequencing algorithm; if those two kinds of matches score differently on the same features, the learned function will not transfer.

Editorial extensions

If this is right

De novo peptide identification can be improved in a post-processing step, without altering the sequencing algorithm or acquisition parameters.
A 10-percentage-point false-positive-rate reduction on 120 spectra implies that re-ranking with a learned scorer can recover correct matches that the original de novo tool had ranked below the top.
The GP-evolved scoring function can be reused as a fitness function inside other de novo sequencing algorithms, not just as a post-hoc re-ranker.
The explicit feature selection in the evolved tree identifies which spectral properties matter most, making the scoring function interpretable rather than a black box.

Reading between the lines

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

A natural extension is to train the same re-ranker on decoy-based labels from database search, removing the need for a validated ground-truth set and allowing much larger training corpora.
Because the scorer never uses the original de novo confidence score, the same model could be applied to candidate lists from any de novo tool; testing on other tools would reveal whether the 10% gain is tied to one tool's candidate distributions.
The fact that most missed correct matches were recovered from lower ranks suggests the bottleneck in de novo identification may be ranking rather than candidate generation, a direction the paper does not explicitly pursue.

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.

Desk Editor's Note

read the letter

The paper applies GP symbolic regression to learn a PSM scoring function from Mascot-derived features, then uses it to re-rank PEAKS de novo candidates. The headline result is a drop in FPR from 0.44 to 0.34 on 120 spectra, driven mainly by recovering 20 of 25 previously missed correct peptides. That is a real empirical result, not a simulated one, and the comparison to RF and SVR is a reasonable sanity check.

What is actually new is modest but real: GP-based symbolic regression for PSM rescoring, with an interpretable tree that discards redundant features. The workflow is clearly described, the feature set is sensible, and the authors are honest that for 28 of the 120 spectra no post-processing can help because the correct peptide is not in the candidate list. The use of an external benchmark dataset is a plus.

The soft spots are in the evaluation. First, the 120-spectrum evaluation set is small, and the paper reports no confidence intervals or significance tests. The difference between GP and SVR is 7 spectra, which could easily be noise. Second, the paper never states that the evaluation set is disjoint from the 10,000-spectrum learning set. Both are sampled from the same full factorial dataset, and without an explicit statement or a list of spectrum identifiers, the possibility of overlap cannot be ruled out. If overlap exists, the model would have seen the correct answer for some evaluation spectra, and the 10% improvement could be inflated. The authors likely intended them to be separate, but the burden is on them to say so. Third, the scoring function is trained on Mascot PSMs and applied to PEAKS PSMs; the paper discusses this transfer in Section 4.3 but does not test whether the feature distributions actually align. That is a known limitation, but it weakens the generality of the result.

Also, Equation 2 has a typo in the text: it says FPR is the ratio of correct matches, but the equation and the tables use false positives. Minor.

None of these flaws are fatal. The central mechanism is not circular: the regression target is Mascot score, and the evaluation is a downstream ranking task on PEAKS candidates. But the evidence is not strong enough to treat the 10% FPR reduction as a stable effect.

Who is this for? Anyone working on post-processing for de novo peptide identification, or on GP applications in bioinformatics. It deserves a serious referee, but I would want the authors to address the disjointness and add error bars or at least a bootstrap.

Recommendation: send to peer review, but flag the evaluation-set overlap and the lack of statistical validation as conditions.

Referee Report

4 major / 5 minor

Summary. The paper proposes GP-PSM, a genetic-programming symbolic-regression scoring function for re-ranking peptide-spectrum matches. It extracts 11 features per PSM and regresses Mascot confidence scores on a learning set of 10,000 MS/MS spectra from the Wessels et al. benchmark, then applies the evolved function to re-score five candidate peptides per spectrum from PEAKS de novo sequencing on 120 evaluation spectra. The central empirical claim is that GP-PSM raises correct top-1 identifications from 67 to 79 of 120 spectra and lowers FPR from 0.44 to 0.34, outperforming RF and SVR re-ranking. The paper also analyzes the evolved tree for feature selection and interpretability.

Significance. If the reported 10-percentage-point FPR reduction is robust, the contribution is a useful, interpretable nonlinear PSM scoring function and a demonstration that GP-based re-ranking can outperform RF/SVR on a real benchmark. The use of a public benchmark with known ground truth, explicit feature definitions, comparison with two ML baselines, and interpretability analysis are strengths. The main limitations are statistical: the headline improvement rests on 120 spectra and a single GP individual selected from 30 runs, with no confidence intervals or significance tests, and the learning/evaluation split is not shown to be disjoint. These issues are fixable, but until addressed the central empirical claim is not established at the standard expected for a journal paper.

major comments (4)

[Section 4.1 / Table 2] The learning set (10,000 spectra) and evaluation set (120 spectra) are both selected from the same full factorial benchmark dataset, but the text never states that the two sets are disjoint. Because the GP model is trained to predict the Mascot score of the known peptide for each training spectrum, any evaluation spectrum that also appeared in training would give the model direct access to the correct answer for that spectrum. This makes the abstract's 10% improvement claim potentially an artifact of training/evaluation overlap. Please state the disjointness explicitly and provide spectrum identifiers or a reproducible split so that the separation can be audited.
[Section 5.2 / Table 5] No confidence intervals or significance tests are reported for the evaluation results. On n=120, the GP/SVR difference is 79 versus 72 correct spectra (FPR 0.34 versus 0.40), a gap well within plausible sampling variability; a paired test such as McNemar's test, or a bootstrap confidence interval on the FPR difference, is needed before 'GP-PSM outperforms RF and SVR' can be supported. In addition, Section 5.1 states that the best GP individual is selected from 30 runs by training RSS, so evaluating that chosen individual on a single 120-spectrum set inherits selection bias; please report the variance of the evaluation metrics across the 30 runs.
[Section 4.3] The transfer assumption is load-bearing but untested. The scoring function is learned from PSMs produced by Mascot database search and applied to PSMs produced by PEAKS de novo sequencing; Section 4.3 only explains that the two sets differ, without any quantitative evidence that their feature distributions overlap. Please provide diagnostics such as feature-distribution comparisons or a cross-training experiment (e.g., train on PEAKS PSMs from a disjoint subset and evaluate), or temper the generalization claim accordingly.
[Section 4.2 / Table 5] The evaluation lacks a baseline that re-ranks the same PEAKS candidate lists using a simple linear combination of the same features, or using raw Mascot scores where available. Such a baseline would isolate the contribution of the GP-evolved nonlinear form from the generic benefit of re-scoring with additional intensity features, and it is needed to support the claim that the GP representation itself is responsible for the observed improvement.

minor comments (5)

[Section 4.2 / Eq. (2)] The sentence preceding Eq. (2) says that FPR is 'the ratio of the number of correct matches to the total number of MS/MS spectra', but the equation defines FPR as FP/N, i.e., the proportion of incorrect top-ranked PSMs. Please correct the sentence so that it matches the equation.
[Section 3.1] The text refers to 'SeqFix and SeqVar, (f11,f12)', but Table 1 lists f10 as SeqFix and f11 as SeqVar; the numbering and naming should be aligned consistently.
[Section 3.1] Please specify the bin width for the fixed-length vectorization features f7-f10 and how the 4,000 bins are defined over the m/z range; without this detail the features are not fully reproducible.
[Section 5.2] There are small typos: 'evolution set' should be 'evaluation set', and 'did not have the change' should be 'did not have the chance'.
[Table 5] Please state explicitly whether 'FPR reduction after post-processing' is reported in absolute percentage points (e.g., 0.10 for GP-PSM) or as a relative reduction; the current text mixes '10% more spectra' with the absolute FPR change from 0.44 to 0.34.

Circularity Check

0 steps flagged · score 2.0 of 10

No substantive circularity; the FPR improvement is an independent transfer result, with only a non-load-bearing self-citation.

full rationale

GP-PSM learns a symbolic-regression model f(X) mapping eleven PSM features to Mascot scores on a 10,000-spectrum learning set (Section 3, Fig. 1), and the reported FPR reduction is obtained by applying this fixed model to PEAKS candidate lists on a separate 120-spectrum evaluation set (Tables 4-5). The evaluation target is the rank of the known correct peptide; no evaluation PSM enters the regression and no parameter is adjusted to the evaluation FPR, so the 10% improvement is an empirical transfer result, not an identity. The only self-citation, [12], supplies provenance for six input features and is not load-bearing. The absence of an explicit statement that the learning and evaluation spectra are disjoint, and the use of Mascot-derived labels both for training and as ground truth, are correctness or validation concerns; under the stated hard rules, without a quoted identity or a fitted parameter renamed as a prediction, they do not establish circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central result rests on the Mascot ground truth, the transfer from Mascot PSMs to PEAKS PSMs, the feature set, and the narrow spectrum filters. GP hyperparameters and vectorization choices are hand-set and affect the evolved function. No new physical entities are introduced.

free parameters (7)

GP population size = 300
Set in Section 3.2; controls the evolutionary search and is not fitted to data.
GP maximum generations = 100
Stopping criterion in Section 3.2; chosen by hand.
Crossover rate = 0.9
Genetic operator rate in Section 3.2; set manually.
Mutation rate = 0.1
Genetic operator rate in Section 3.2; set manually.
Tournament size = 5
Selection pressure in Section 3.2; set manually.
Fragment ion tolerance for variable-length vectorization = 0.5 Da
Used for the SeqVar feature in Section 3.1; determines the bin width of the variable-length spectrum vector.
Fixed vector length for features f7 to f10 = 4000 bins
Set in Section 3.1; determines bin granularity for the fixed-length vectorized features.

assumptions (5)

domain assumption Mascot database search identifications with q-value <= 0.01 are correct ground truth.
The learning labels, regression target, and evaluation correctness all use Mascot results from the full factorial dataset (Section 3 and Section 4.1).
domain assumption A scoring function trained on Mascot-generated PSMs transfers to PEAKS de novo candidate PSMs.
The train/test PSMs come from Mascot, while the evaluation PSMs come from PEAKS; the paper assumes the feature distributions are similar enough for re-ranking (Section 4.3).
domain assumption The 11 features in Table 1 are sufficient to discriminate correct from incorrect PSMs.
No feature ablation or external validation beyond the single evaluation set is provided, so the adequacy of the feature set is assumed.
domain assumption The selected spectrum population (doubly charged, precursor mass <=1150 Da, peptide length 7-12, no modifications) is the intended scope.
These filters are imposed in Section 4.1 and the claim is only tested within this narrow slice of MS/MS data.
domain assumption Arithmetic operators with protected division are sufficient to express a good scoring function.
The GP function set in Section 3.2 restricts the search space to these operators.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches." pith.science (2026). https://pith.science/paper/IFD7IYW6

@misc{pith2026190808010,
  author       = {Pith},
  title        = {Pith review of: Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFD7IYW6}},
  note         = {Machine review of arXiv:1908.08010}
}

read the original abstract

De novo peptide sequencing algorithms have been widely used in proteomics to analyse tandem mass spectra (MS/MS) and assign them to peptides, but quality-control methods to evaluate the confidence of de novo peptide sequencing are lagging behind. A fundamental part of a quality-control method is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). Here, we propose a genetic programming (GP) based method, called GP-PSM, to learn a PSM scoring function for improving the rate of confident peptide identification from MS/MS data. The GP method learns from thousands of MS/MS spectra. Important characteristics about goodness of the matches are extracted from the learning set and incorporated into the GP scoring functions. We compare GP-PSM with two methods including Support Vector Regression (SVR) and Random Forest (RF). The GP method along with RF and SVR, each is used for post-processing the results of peptide identification by PEAKS, a commonly used de novo sequencing method. The results show that GP-PSM outperforms RF and SVR and discriminates accurately between correct and incorrect PSMs. It correctly assigns peptides to 10% more spectra on an evaluation dataset containing 120 MS/MS spectra and decreases the false positive rate (FPR) of peptide identification.

Figures

Figures reproduced from arXiv: 1908.08010 by the authors.

**Figure 1.** The workflow of the proposed GP-PSM method consisting of three phases. GP searched for the solutions during the evolutionary search process. A set of genetic operators is applied on the individuals to generate fitter offsprings for the next generation [10]. GP simulates evolution by employing fitness based selection where the fittest program is expected to be chosen. The process can be stopped based on the stopping… view at source ↗

**Figure 2.** The best GP evolved program (the PSM ranking function). each is used for post-processing the results of peptide identification by PEAKS, a commonly used de novo sequencing method. The results show that GP-PSM outperforms RF and SVR and discriminates accurately between correct and incorrect PSMs. It correctly assigns peptides to 10% more spectra on an evaluation dataset containing 120 MS/MS spectra and decreases the… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

[1]

psite: Amino acid conﬁdence evaluation for quality control of de novo peptide sequencing and modiﬁcation site localization

Hao Yang, Hao Chi, Wen-Jing Zhou, Wen-Feng Zeng, Chao Liu, Rui-Min Wang, Zhao-Wei Wang, Xiu-Nan Niu, Zhen-Lin Chen, and Si-Min He. psite: Amino acid conﬁdence evaluation for quality control of de novo peptide sequencing and modiﬁcation site localization. Journal of proteome research , 17(1):119–128, 2017

work page 2017
[2]

Introduction to computational proteomics

Jacques Colinge and Keiryn L Bennett. Introduction to computational proteomics. PLoS Comput Biol , 3(7):e114, 2007. Genetic Programming for Scoring Peptide-Spectrum Matches 13

work page 2007
[3]

A method for assessing the statistical sig- niﬁcance of mass spectrometry-based protein identiﬁcations using general scoring schemes

David Feny¨ o and Ronald C Beavis. A method for assessing the statistical sig- niﬁcance of mass spectrometry-based protein identiﬁcations using general scoring schemes. Analytical chemistry, 75(4):768–774, 2003

work page 2003
[4]

Em- pirical statistical model to estimate the accuracy of peptide identiﬁcations made by MS/MS and database search

Andrew Keller, Alexey I Nesvizhskii, Eugene Kolker, and Ruedi Aebersold. Em- pirical statistical model to estimate the accuracy of peptide identiﬁcations made by MS/MS and database search. Analytical chemistry, 74(20):5383–5392, 2002

work page 2002
[5]

Genetic programming as a model induction engine

Vladan Babovic and Maarten Keijzer. Genetic programming as a model induction engine. Journal of Hydroinformatics , 2(1):35–60, 2000

work page 2000
[6]

Pareto-front exploitation in symbolic regres- sion

Guido Smits and Mark Kotanchek. Pareto-front exploitation in symbolic regres- sion. Genetic programming theory and practice II , pages 283–299, 2005

work page 2005
[7]

Building credit scoring models using genetic programming

Chorng-Shyong Ong, Jih-Jeng Huang, and Gwo-Hshiung Tzeng. Building credit scoring models using genetic programming. Expert Systems with Applications , 29(1):41–47, 2005

work page 2005
[8]

Forecasting energy consumption using a grey model improved by incorporating genetic programming

Yi-Shian Lee and Lee-Ing Tong. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1):147–152, 2011

work page 2011

Show all 17 references

[9]

Search based software engineering for software product line engineering: a survey and directions for future work

Mark Harman, Yue Jia, Jens Krinke, William B Langdon, Justyna Petke, and Yuanyuan Zhang. Search based software engineering for software product line engineering: a survey and directions for future work. In Proceedings of the 18th International Software Product Line Conference-...

2014
[10]

Genetic programming: An introduction and tutorial, with a survey of techniques and appli- cations

William B Langdon, Riccardo Poli, Nicholas F McPhee, and John R Koza. Genetic programming: An introduction and tutorial, with a survey of techniques and appli- cations. In Computational intelligence: A compendium , pages 927–1028. Springer, 2008

2008
[11]

Peptide fragmentation overview

Richard L Beardsley Herrmann and Amye Hilderbrand. Peptide fragmentation overview. Principles of Mass Spectrometry Applied to Biomolecules , 10:279, 2006

2006
[12]

GA-Novo: De novo peptide sequencing via tandem mass spectrometry using genetic algorithm

Samaneh Azari, Bing Xue, Mengjie Zhang, and Lifeng Peng. GA-Novo: De novo peptide sequencing via tandem mass spectrometry using genetic algorithm. In Applications of Evolutionary Computation , pages 72–89, Cham, 2019

2019
[13]

An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database

Jimmy K Eng, Ashley L McCormack, and John R Yates. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry , 5(11):976–989, 1994

1994
[14]

DEAP: Evolutionary algorithms made easy.Jour- nal of Machine Learning Research , 13:2171–2175, 2012

F´ elix-Antoine Fortin, Fran¸ cois-Michel De Rainville, Marc-Andr´ e Gardner, Marc Parizeau, and Christian Gagn´ e. DEAP: Evolutionary algorithms made easy.Jour- nal of Machine Learning Research , 13:2171–2175, 2012

2012
[15]

A comprehensive full factorial lc-ms/ms proteomics benchmark data set

Hans JCT Wessels, Tom G Bloemberg, Maurice van Dael, Ron Wehrens, Lutgarde Buydens, Lambert P van den Heuvel, and Jolein Gloerich. A comprehensive full factorial lc-ms/ms proteomics benchmark data set. Proteomics, 12(14):2276–2281, 2012

2012
[16]

Probability-based protein identiﬁcation by search- ing sequence databases using mass spectrometry data.electrophoresis, 20(18):3551– 3567, 1999

John S Cottrell and U London. Probability-based protein identiﬁcation by search- ing sequence databases using mass spectrometry data.electrophoresis, 20(18):3551– 3567, 1999

1999
[17]

Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry

Bin Ma, Kaizhong Zhang, Christopher Hendrie, Chengzhi Liang, Ming Li, Amanda Doherty-Kirby, and Gilles Lajoie. Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid communications in mass spec- trometry, 17(20):2337–2342, 2003

2003

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

psite: Amino acid conﬁdence evaluation for quality control of de novo peptide sequencing and modiﬁcation site localization

Hao Yang, Hao Chi, Wen-Jing Zhou, Wen-Feng Zeng, Chao Liu, Rui-Min Wang, Zhao-Wei Wang, Xiu-Nan Niu, Zhen-Lin Chen, and Si-Min He. psite: Amino acid conﬁdence evaluation for quality control of de novo peptide sequencing and modiﬁcation site localization. Journal of proteome research , 17(1):119–128, 2017

work page 2017

[2] [2]

Introduction to computational proteomics

Jacques Colinge and Keiryn L Bennett. Introduction to computational proteomics. PLoS Comput Biol , 3(7):e114, 2007. Genetic Programming for Scoring Peptide-Spectrum Matches 13

work page 2007

[3] [3]

A method for assessing the statistical sig- niﬁcance of mass spectrometry-based protein identiﬁcations using general scoring schemes

David Feny¨ o and Ronald C Beavis. A method for assessing the statistical sig- niﬁcance of mass spectrometry-based protein identiﬁcations using general scoring schemes. Analytical chemistry, 75(4):768–774, 2003

work page 2003

[4] [4]

Em- pirical statistical model to estimate the accuracy of peptide identiﬁcations made by MS/MS and database search

Andrew Keller, Alexey I Nesvizhskii, Eugene Kolker, and Ruedi Aebersold. Em- pirical statistical model to estimate the accuracy of peptide identiﬁcations made by MS/MS and database search. Analytical chemistry, 74(20):5383–5392, 2002

work page 2002

[5] [5]

Genetic programming as a model induction engine

Vladan Babovic and Maarten Keijzer. Genetic programming as a model induction engine. Journal of Hydroinformatics , 2(1):35–60, 2000

work page 2000

[6] [6]

Pareto-front exploitation in symbolic regres- sion

Guido Smits and Mark Kotanchek. Pareto-front exploitation in symbolic regres- sion. Genetic programming theory and practice II , pages 283–299, 2005

work page 2005

[7] [7]

Building credit scoring models using genetic programming

Chorng-Shyong Ong, Jih-Jeng Huang, and Gwo-Hshiung Tzeng. Building credit scoring models using genetic programming. Expert Systems with Applications , 29(1):41–47, 2005

work page 2005

[8] [8]

Forecasting energy consumption using a grey model improved by incorporating genetic programming

Yi-Shian Lee and Lee-Ing Tong. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1):147–152, 2011

work page 2011