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REVIEW 4 major objections 5 minor 53 references

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

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

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Pith's one-line read This paper argues that supervised prediction of human aging-related genes improves when the classifier uses an aging-specific protein–protein interaction subnetwork rather than the entire static interactome.

desk verdict A legitimate first application of supervised prediction to an aging-specific PPI subnetwork, but test-set model selection and label leakage break the reported accuracy comparisons. read the letter →

arxiv 1908.08135 v4 pith:ILDPMV5S submitted 2019-08-21 q-bio.MN

Qi Li , Tijana Milenković This is my paper · ORCID
classification q-bio.MN
keywords aging-relatedgenesprotein-proteininteractionnetworkcontext-specificsubnetworksupervisednodeclassificationgraphletfeaturesdynamicGenAgegeneexpressionintegration
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 AGENT, a supervised framework that predicts human aging-related genes from a protein–protein interaction (PPI) subnetwork restricted to aging context, rather than from the full static interactome. It claims that this context-specific subnetwork generally yields more accurate predictions than the entire network, and that graphlet- and centrality-based node features used here for the first time in this task outperform existing UniNet and mBPIs features. The claims are tested with 5-fold cross-validation and by checking whether newly predicted genes appear among known cancer driver genes. The authors report that the subnetwork advantage holds in most evaluation tests, while the dynamic version of the subnetwork is not consistently better than its static version. However, when the ground truth is redefined using human expression data (GTEx-DAG), every method performs at chance, which the authors report as surprising and leave for future work.

What carries the argument

The load-bearing object is the aging-specific PPI subnetwork: a dynamic network of 37 age-specific snapshots, each an induced subgraph on genes significantly expressed at that age, plus a static version aggregating all snapshots. On these networks the paper computes node features that capture network position and its change over time—dynamic graphlet degree vectors, graphlet orbit transitions, centrality trajectories, static graphlet degree vectors, and colored graphlet degree vectors—and compares them against the UniNet centrality vector and the 30BPIs highest-degree-neighbor indicator under nine classifiers and eight dimensionality choices. The aging-specific subnetwork is the thing being tested: the argument runs by showing that features computed on it beat the same features, and the baselines, on the entire context-unspecific interactome from which it was derived.

What would settle it

Rebuild the aging-specific subnetwork from a modern PPI interactome and RNA-seq expression data, rerun the same features against GTEx-DAG labels, and check whether any feature exceeds random AUPR; if none does, the context-specific advantage is confined to GenAge labels and the older network, while if some do, the chance-level result in Section 3.6 was an artifact of outdated data.

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Extended reading notes

Core claim

The central claim is that context matters: a supervised classifier for aging-related genes performs better, in most evaluation tests, when trained on a protein–protein interaction subnetwork restricted to aging-specific gene expression than when trained on the full static interactome. The authors evaluate fourteen feature–network combinations, pairing graphlet-based features (dynamic graphlet degree vectors, graphlet orbit transitions, static and colored graphlet degree vectors) and centrality-based features (eccentricity, k-core, degree, centrality mean and variation) with nine classifiers and eight dimensionality choices. Their best results come from features on aging-specific subnetworks: in terms of precision, dynamic DGDV on the dynamic subnetwork and SGDV and cSGDV on the static subnetwork outperform every feature on the entire network, and they also outperform the UniNet and 30BPIs baselines. The dynamic subnetwork does not consistently beat its static version, so the paper treats hypotheses 1 and 2—context-specific network and new features—as the contributions, not the dynamic aspect. When the ground truth is switched from GenAge to expression-derived GTEx-DAG labels, all features perform at chance, a result the authors report as surprising and leave for future work.

Load-bearing premise

The results stand or fall with GenAge as the definition of aging-related genes: when that label set is replaced by human-expression-derived GTEx-DAG labels, every method drops to chance, so the reported predictive advantage is contingent on GenAge being the right ground truth.

Editorial extensions

If this is right

  • If hypothesis 1 is correct, future computational prioritization of aging genes should use a context-specific subnetwork rather than the full interactome as the default network representation.
  • If hypothesis 2 is correct, graphlet-based node features—especially SGDV and cSGDV on the static aging-specific subnetwork—are better precision-oriented tools for this task than the UniNet and 30BPIs baselines.
  • The small overlaps between different features' newly predicted genes imply the features encode complementary topological information, so combining them, for example in an ensemble, should recover more true aging-related genes than any single feature.
  • The chance-level accuracy under GTEx-DAG labels means that with current networks, expression-derived human aging labels are not predictable from PPI topology, so the paper's reported advantage is specific to GenAge-defined aging genes.
  • The dynamic subnetwork's failure to consistently outperform the static one suggests that temporal aggregation alone does not improve supervised aging-gene classification, and better dynamic subnetwork inference would be needed to test the dynamic hypothesis.

Reading between the lines

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

  • A testable extension the paper leaves implicit is to infer an aging-specific subnetwork via network propagation from a recent interactome and then check whether GTEx-DAG labels become predictable; the authors flag network propagation and newer data as future work but do not run this test.
  • The complementarity of predictions across features suggests that an ensemble or multi-view classifier would likely outperform every single feature; the paper mentions this as promising rather than testing it.
  • The GTEx-DAG result is consistent with a technology- or annotation-bias interpretation: the older HPRD network and microarray-derived subnetworks may embed sequence-based knowledge better than expression-based knowledge, which would explain why GenAge labels are learnable while GTEx-DAG labels are not.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes AGENT, a supervised machine-learning framework for predicting human aging-related genes from an aging-specific protein-protein interaction (PPI) subnetwork. The subnetwork is derived by integrating age-specific gene expression data with the HPRD static PPI network, and the framework uses eleven network-based node features (seven dynamic, two static, plus the existing UniNet and mBPIs features) combined with nine classifiers and eight dimensionality-reduction choices. The central empirical claims are that (i) using the aging-specific subnetwork yields more accurate predictions than using the entire static PPI network, and (ii) the newly used features outperform existing UniNet and mBPIs. Evaluation is performed primarily with 5-fold cross-validation on a GenAge-based label set, reporting AUPR, precision, recall, and F-score, with additional validation on cancer gene sets. The paper also reports a secondary analysis using human expression-derived GTEx-DAG labels, where all methods perform near chance. Overall, the paper is comprehensive and richly detailed, but the validity of its central claims depends critically on the soundness of its evaluation protocol.

Significance. If the empirical claims were valid, the paper would make a useful contribution by demonstrating that context-specific PPI subnetworks, built by integrating expression data with interaction data, carry predictive signal for supervised aging-gene classification that a static interactome does not. It is also one of the first systematic supervised frameworks for this task on such subnetworks, and it introduces several graphlet- and centrality-based features to the aging-prediction domain. The paper includes a valuable negative result (GTEx-DAG chance-level performance) and openly discusses limitations. However, the significance is conditional on the evaluation being unbiased; the central comparison is currently compromised by test-set leakage in model selection and by label-derived features, which means the reported advantages and p-values do not establish the paper's main hypotheses.

major comments (4)
  1. [2.3.1] The model-selection protocol leaks test information. For each feature, the dimensionality_choice–classifier combination that maximizes AUPR is chosen using the same 5-fold cross-validation predictions that are subsequently reported for AUPR, precision, recall, and F-score, and that are used in the paired Wilcoxon tests (Section 2.3.3). This is effectively selecting the best of 72 configurations on the evaluation folds; the reported metrics are maxima over a noisy estimate on the test data, so they are optimistically biased, and the reported adjusted p-values are invalid. This bias directly affects the comparisons supporting hypotheses 1 and 2 in Sections 3.2 and 3.3. The authors should use a nested cross-validation or otherwise ensure that model selection is performed only on training folds, with the test folds reserved strictly for final evaluation.
  2. [2.2.1] Two features encode the aging ground truth directly and therefore leak test labels into training. cSGDV colors every node in the network (except the node currently being featurized) with one of three colors based on its GenAge status from Section 2.1.3, and these colors are computed on the full network before any train/test split. For a test node, its cSGDV feature therefore includes GenAge membership of other test nodes, which is label information from the test set. Similarly, UniNet's feature includes 'aging neighbor count' and 'aging neighbor ratio' computed from GenAge labels of all network nodes. Because these features are extracted before cross-validation, the labels of test nodes enter the feature vectors used for training, which can manufacture above-chance separation on its own. Features should be recomputed within each cross-validation fold using only training-node labels (and the node's own label should not be used at all).
  3. [2.1.1] The choice of the entire context-unspecific network is made after comparing performance on HPRD versus BioGRID, and HPRD is retained because it gives better results ('performance is better on the HPRD network than on the BioGRID network (results not shown)'). This is outcome-based data selection: the baseline 'entire network' is chosen because it is favorable to the paper's hypotheses. Since the aging-specific subnetwork is derived from HPRD, the comparison between subnetwork and entire network is not a fair test of the hypothesis that context specificity helps; a different entire network might change the results. The authors should report the BioGRID comparison in full, or justify the network choice on independent grounds, ideally by evaluating all hypotheses on both entire networks.
  4. [3.6] The central claim is contingent on the GenAge ground truth. When a human expression-derived definition (GTEx-DAG) is used instead, all features perform at chance and none is significantly better than random (adjusted p-values ≥ 0.054). The paper reports this negative result, but the abstract's general assertion that 'using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network' is not supported under this alternative label set. The conclusions should be explicitly scoped to the GenAge-based definition, and the authors should discuss more thoroughly whether the method offers any advantage for predicting human expression-derived aging genes, given that the reported advantage disappears in that setting.
minor comments (5)
  1. [1.1] There is a typo in the phrase 'we find hypotheis 3 to hold' where 'hypothesis' is misspelled.
  2. [2.1.1] The BioGRID comparison is mentioned as 'results not shown'; since this comparison is used to justify a load-bearing data choice, the results should be included at least in a supplement.
  3. [2.2.1] The text sometimes uses 'cGDV' instead of 'cSGDV' (for example, in the Figure 2 caption and in Section 3.1), which could confuse readers; please standardize the notation.
  4. [2.2.1] The explanation for why GDC uses up to 5-node graphlets (a limitation of the executable) is unusual; it would be helpful to state explicitly whether this choice could affect comparability with the other graphlet-based features.
  5. [3.1] In Table 2, the notation 'X + Y (Z)' is used, but for PCA the dimension Z varies across folds and is averaged; this should be stated directly in the table caption or in the surrounding text.

Circularity Check

3 steps flagged · score 6.0 of 10

Partial circularity: cSGDV and UniNet features encode the GenAge target labels directly, and the best model per feature is selected on the same cross-validation folds used for evaluation, inflating the reported comparisons.

  1. self definitional [Section 2.2.1, cSGDV feature description]
    "When computing a node v’s cGDV , we do not want to leak the information of whether node v is aging-related or not to its feature, as this could cause a circular argument during classification. So, we ignore such information entirely by assigning a “neutral” color, referred to as color 1, only to node v out of all nodes in the network when computing v’s cGDV . At the same time, we label each of the other nodes in the network with one of three other colors, depending on which of the following three gene sets it belongs to according to Section 2.1.3."

    The cSGDV feature for every node is built by coloring all other nodes according to the same GenAge-based labels that the classifier is asked to predict. Features are computed on the full network before the 5-fold split (Section 2.3.1), so the labels of nodes in the test fold are used to construct the features of other nodes, including training nodes. The paper explicitly avoids leaking a node's own label by giving it a neutral color, but it does not avoid cross-node label leakage. Consequently, cSGDV's ability to predict GenAge status is partly a deterministic function of GenAge labels themselves; its reported significant superiority in Section 3.2 is not independent evidence for hypothesis 1.

  2. self definitional [Section 2.2.1, UniNet feature description]
    "UniNet’s feature [8] combines 14 node centralities: DegC, ECC, KC, average shortest path, betweenness, closeness, clustering coefficient, neighborhood connectivity, radiality, stress, topological coefficient, aging neighbor count, aging neighbor ratio, and binary aging neighbor representations (for details, see the UniNet publication [8])."

    The 'aging neighbor count' and 'aging neighbor ratio' dimensions require knowing which neighboring nodes are aging-related, which in this paper is exactly the GenAge target label defined in Section 2.1.3. Because all features are computed from the full network before the train/test split, the GenAge status of test-fold genes enters the feature vectors of other genes. UniNet on the static aging-specific subnetwork is one of the features cited in Section 3.2 as making hypothesis 1 hold in precision, so that support is partly self-definitional.

1 more flagged steps
  1. fitted input called prediction [Sections 2.3.1 and 2.3.3]
    "We compute these measures for each prediction set, i.e., at each value of g. We summarize the performance of a feature–dimensionality_choice–classifier combination over the entire g range via the area under the precision-recall curve (AUPR). For each feature, we choose the dimensionality_choice–classifier combination that maximizes the AUPR."

    The AUPR that is maximized is computed on the same 5-fold cross-validation predictions that are later used to report precision, recall, and F-score and to run the paired Wilcoxon tests (Section 2.3.3). Thus the reported AUPR for each feature is a maximum over 72 configurations evaluated on the test folds themselves, not an unbiased estimate of predictive performance. All p-values are conditional on this test-set model selection, which inflates the comparisons used to support hypotheses 1 and 2, including comparisons involving label-free features such as DGDV and SGDV.

full rationale

The paper's contribution is an empirical evaluation rather than a formal derivation, so the circularity question is about whether the reported predictions are independent of the labels they claim to predict. The clearest circular steps are in feature construction: cSGDV colors every node except the target node by the GenAge target label, and UniNet includes aging-neighbor count/ratio features that by definition require the same labels; both are computed on the full network before cross-validation, so test labels leak into features. A second, independent leakage channel is the model-selection protocol: the dimensionality/classifier combination for each feature is chosen by maximizing AUPR on the exact folds used for the reported accuracy and p-values, making the headline statistics optimistically biased. These problems are partial rather than total: DGDV, SGDV, GDC, GoT, centrality features, and 30BPIs are not label-derived, and the paper honestly reports the GTEx-DAG negative control where all methods perform at chance. Self-citations to [4], [11], and [25] supply data and feature definitions but are not used as a uniqueness theorem or as a substitute for evaluation, so they do not by themselves raise the score. Overall, the central claim that the aging-specific subnetwork improves prediction is supported in part by label-encoding features and by metrics and p-values from a leakage-prone selection protocol, corresponding to partial circularity. The cancer-data validation is an external check, but it uses prediction sets and thresholds already selected under the same biased protocol, so it does not fully remove the circularity.

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

The framework's central claim rests on the GenAge ground truth, the aging-specific subnetwork from prior work, and the validity of network and cancer data as proxies; these are domain assumptions rather than derived results. The free parameters are mostly hyperparameters and thresholds selected during the evaluation, the most consequential being the per-feature model selection on the test folds.

free parameters (5)
  • mBPIs parameter m = 30
    Three values (10, 20, 30) were tested in preliminary analyses and m=30 performed best; all reported results use m=30 (Section 2.2.1).
  • tSNE perplexity = best of {5, 13, 21, 30, 40, 50} selected per feature
    Six perplexities tested; for each feature the perplexity maximizing AUPR was chosen within the same cross-validation used for evaluation (Section 2.2.2).
  • PCA variance threshold = 90%
    PCA components retained to account for at least 90% of variation; the resulting dimension varies across folds (Section 2.2.2).
  • Prediction threshold g = g where F-score is maximized per feature
    For each feature, the number of top predictions g is selected on the cross-validation data; all precision/recall/F-score and cancer validation results depend on this threshold (Section 2.3.1).
  • Graphlet signature sizes = 4 nodes, 6 events (DGDV); 4 nodes (GoT); 5 nodes (GDC)
    Graphlet signature sizes inherited from prior publications; GDC uses the executable default of up to 5-node graphlets because the code does not allow specification (Section 2.2.1).
assumptions (5)
  • domain assumption GenAge contains the correct set of human aging-related genes
    Section 2.1.3 uses the 187 GenAge genes as the primary positive labels; the framework's accuracy is measured against these labels.
  • domain assumption The aging-specific subnetwork from Faisal and Milenković (2014) is a valid aging-specific biological network
    Section 1.1 states the paper relies on the existing dynamic aging-specific subnetwork without re-evaluating the inference method; the entire comparison rests on this network being more aging-relevant than HPRD.
  • domain assumption Cancer-related gene sets are a valid independent validation for aging-related predictions
    Section 2.3.4 validates newly predicted aging genes via overlap with cancer driver genes, assuming the aging-cancer link is strong enough to serve as ground truth.
  • domain assumption Genes are exchangeable samples in cross-validation despite being connected in the same network
    The 5-fold CV splits genes randomly, ignoring network autocorrelation and shared features derived from the same network; this can inflate accuracy.
  • domain assumption Node features computed from the full network do not leak test labels into training
    Features such as cSGDV and UniNet's aging neighbor counts are computed using ground-truth aging labels of other nodes across the whole network before splitting into folds; no fold-aware feature recomputation is described.

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Cite this review

Pith. "Pith review of Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork." pith.science (2026). https://pith.science/paper/ILDPMV5S

@misc{pith2026190808135,
  author       = {Pith},
  title        = {Pith review of: Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ILDPMV5S}},
  note         = {Machine review of arXiv:1908.08135}
}
read the original abstract

Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. Results. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. Conclusion. These results justify the need for our framework.

Figures

Figures reproduced from arXiv: 1908.08135 by the authors.

Figure 1
Figure 1. Summary of AGENT and our study. Note that features 1-7 work only on the dynamic aging-specific subnetwork. Features 8-11 work on the static aging-specific subnetwork as well as the entire static context-unspecific network. So, in total, there exist 7 + 4 + 4 = 15 feature-network combinations. However, we could not run one of the features (cSGDV) on the entire context-unspecific network, as the large size of this net… view at source ↗
Figure 2
Figure 2. The precision-recall curves of all considered features (each under the best dimensionality choice and classifier) when run on their respective networks, plus the random approach, when using GenAge to define aging- and non-aging-related genes, and when reducing feature dimensionality during the 5-fold cross-validation. Panel (a) is for the seven dynamic features when run on the dynamic aging-specific subnetwork. Pane… view at source ↗
Figure 3
Figure 3. Prediction accuracy in the 5-fold cross-validation in terms of AUPR of all considered features (each under the best dimensionality choice and classifier) when run on their respective networks, plus the random approach, when using GenAge to define aging- and non-aging-related genes, and when reducing feature dimensionality during the 5-fold cross-validation. The seven dynamic features can be run only on the dynamic s… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Prediction accuracy in the 5-fold cross-validation in terms of precision, recall, and F-score of all considered features (each under the best dimensionality choice and classifier) when run on their respective networks, plus the random approach, when using GenAge to def…
Figure 5
Figure 5. Figure 5: Prediction accuracy in the validation of newly predicted genes via all driver cancer genes, in terms of precision, recall, and F-score of all considered features (each under the best dimensionality choice and classifier) when run on their respective networks, when usin…
Figure 6
Figure 6. Figure 6: Overlaps of the cancer data-validated newly predicted genes resulting from the three networks, when using GenAge to define aging- and non-aging-related genes, and when reducing feature dimensionality during the 5-fold cross-validation. Each of the three prediction sets…

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Reference graph

Works this paper leans on

53 extracted references · 50 canonical work pages

  1. [1]

    Aging, cellular senescence, and cancer

    Judith Campisi. Aging, cellular senescence, and cancer. Annual Review of Physiology, 75:685–705, 2013

    work page 2013

  2. [2]

    The aging kidney revisited: a systematic review

    Davide Bolignano, Francesco Mattace-Raso, Eric JG Sijbrands, and Carmine Zoccali. The aging kidney revisited: a systematic review. Ageing Research Reviews, 14:65–80, 2014

    work page 2014

  3. [3]

    A review of supervised machine learning applied to ageing research

    Fabio Fabris, João Pedro De Magalhães, and Alex A Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2):171–188, 2017

    work page 2017

  4. [4]

    Dynamic networks reveal key players in aging.Bioinformatics, 30(12):1721– 1729, 2014

    Fazle E Faisal and Tijana Milenkovi´c. Dynamic networks reveal key players in aging.Bioinformatics, 30(12):1721– 1729, 2014

    work page 2014

  5. [5]

    The search for longevity and healthy aging genes: insights from epidemiological studies and samples of long-lived individuals

    Joanne M Murabito, Rong Yuan, and Kathryn L Lunetta. The search for longevity and healthy aging genes: insights from epidemiological studies and samples of long-lived individuals. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 67(5):470–479, 2012

    work page 2012

  6. [6]

    Improving identification of key players in aging via network de-noising and core inference

    Boyoung Yoo, Fazle Elahi Faisal, Huili Chen, and Tijana Milenkovi´c. Improving identification of key players in aging via network de-noising and core inference. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 14(5):1056–1069, 2017

    work page 2017

  7. [7]

    A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related

    Alex A Freitas, Olga Vasieva, and João Pedro de Magalhães. A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related. BMC Genomics, 12(1):27, 2011. 17

    work page 2011

  8. [8]

    Prediction and characterization of human ageing-related proteins by using machine learning

    Csaba Kerepesi, Bálint Daróczy, Ádám Sturm, Tibor Vellai, and András Benczúr. Prediction and characterization of human ageing-related proteins by using machine learning. Scientific Reports, 8(1):4094, 2018

    work page 2018

Show all 53 references
  1. [9]

    Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing

    Robi Tacutu, Thomas Craig, Arie Budovsky, Daniel Wuttke, Gilad Lehmann, Dmitri Taranukha, Joana Costa, Vadim E Fraifeld, and Joao Pedro De Magalhaes. Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Research, ...

    2012

  2. [10]

    Global network alignment in the context of aging.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(1):40–52, 2014

    Fazle Elahi Faisal, Han Zhao, and Tijana Milenkovi´c. Global network alignment in the context of aging.IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(1):40–52, 2014

    2014

  3. [11]

    From homogeneous to heterogeneous network alignment via colored graphlets

    Shawn Gu, John Johnson, Fazle E Faisal, and Tijana Milenkovi´c. From homogeneous to heterogeneous network alignment via colored graphlets. Scientific Reports, 8(1):12524, 2018

    2018

  4. [12]

    An analysis of aging-related genes derived from the genotype-tissue expression project (GTEx)

    Kaiwen Jia, Chunmei Cui, Yuanxu Gao, Yuan Zhou, and Qinghua Cui. An analysis of aging-related genes derived from the genotype-tissue expression project (GTEx). Cell Death Discovery, 5(1):26, 2018

    2018

  5. [13]

    Gene regulation and DNA damage in the ageing human brain

    Tao Lu, Ying Pan, Shyan-Yuan Kao, Cheng Li, Isaac Kohane, Jennifer Chan, and Bruce A Yankner. Gene regulation and DNA damage in the ageing human brain. Nature, 429(6994):883, 2004

    2004

  6. [14]

    Gene expression changes in the course of normal brain aging are sexually dimorphic

    Nicole C Berchtold, David H Cribbs, Paul D Coleman, Joseph Rogers, Elizabeth Head, Ronald Kim, Tom Beach, Carol Miller, Juan Troncoso, John Q Trojanowski, et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proceedings of the National Aca...

    2008

  7. [15]

    Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis

    Inge R Holtman, Divya D Raj, Jeremy A Miller, Wandert Schaafsma, Zhuoran Yin, Nieske Brouwer, Paul D Wes, Thomas Möller, Marie Orre, Willem Kamphuis, et al. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression met...

    2015

  8. [16]

    Microarray analysis of the astrocyte transcriptome in the aging brain: relationship to alzheimer’s pathology and apoe genotype.Neurobiology of Aging, 32(10):1795–1807, 2011

    Julie E Simpson, Paul G Ince, Pamela J Shaw, Paul R Heath, Rohini Raman, Claire J Garwood, Catherine Gelsthorpe, Lynne Baxter, Gillian Forster, Fiona E Matthews, et al. Microarray analysis of the astrocyte transcriptome in the aging brain: relationship to alzheimer’s pathology...

    2011

  9. [17]

    Classifying aging genes into DNA repair or non-DNA repair-related categories

    Yaping Fang, Xinkun Wang, Elias K Michaelis, and Jianwen Fang. Classifying aging genes into DNA repair or non-DNA repair-related categories. In International Conference on Intelligent Computing, pages 20–29. Springer, 2013

    2013

  10. [18]

    An extensive empirical comparison of probabilistic hierar- chical classifiers in datasets of ageing-related genes

    Fabio Fabris, Alex A Freitas, and Jennifer Tullet. An extensive empirical comparison of probabilistic hierar- chical classifiers in datasets of ageing-related genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(6):1045–1058, 2016

    2016

  11. [19]

    Network model of human aging: Frailty limits and information measures

    Spencer G Farrell, Arnold B Mitnitski, Kenneth Rockwood, and Andrew D Rutenberg. Network model of human aging: Frailty limits and information measures. Physical Review E, 94(5):052409, 2016

    2016

  12. [20]

    Understanding the odd science of aging

    Thomas BL Kirkwood. Understanding the odd science of aging. Cell, 120(4):437–447, 2005

    2005

  13. [21]

    Human protein interaction networks across tissues and diseases

    Esti Yeger-Lotem and Roded Sharan. Human protein interaction networks across tissues and diseases. Frontiers in Genetics, 6:257, 2015

    2015

  14. [22]

    Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes

    Chao Wu, Jun Zhu, and Xuegong Zhang. Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC Bioinformatics, 13(1):182, 2012

    2012

  15. [23]

    Identification of co-evolving temporal networks

    Rasha Elhesha, AAisharjya Sarkar, Christina Boucher, , and Tamer Kahveci. Identification of co-evolving temporal networks. BMC Genomics, 20(434), 2019

    2019

  16. [24]

    Human protein reference database—2009 update

    TS Prasad, Renu Goel, Kumaran Kandasamy, Shivakumar Keerthikumar, Sameer Kumar, Suresh Mathivanan, Deepthi Telikicherla, Rajesh Raju, Beema Shafreen, Abhilash Venugopal, et al. Human protein reference database—2009 update. Nucleic Acids Research, 37(suppl_1):D767–D772, 2009

    2009

  17. [25]

    Exploring the structure and function of temporal networks with dynamic graphlets

    Yuriy Hulovatyy, Huili Chen, and Tijana Milenkovi´c. Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics, 31(12):i171–i180, 2015

    2015

  18. [26]

    Graphlet-orbit transitions (GoT): A fingerprint for temporal network comparison

    David Aparício, Pedro Ribeiro, and Fernando Silva. Graphlet-orbit transitions (GoT): A fingerprint for temporal network comparison. PlOS ONE, 13(10):e0205497, 2018

    2018

  19. [27]

    Dominating biological networks

    Tijana Milenkovi´c, Vesna Memiševi´c, Anthony Bonato, and Nataša Pržulj. Dominating biological networks. PlOS ONE, 6(8):e23016, 2011

    2011

  20. [28]

    Uncovering biological network function via graphlet degree signatures

    Tijana Milenkovi´c and Nataša Pržulj. Uncovering biological network function via graphlet degree signatures. Cancer Informatics, 6:CIN–S680, 2008. 18

    2008

  21. [29]

    Graphlets in network science and computational biology

    Khalique Newaz and Tijana Milenkovi´c. Graphlets in network science and computational biology. Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists, Cambridge University Press, page 193, 2019

    2019

  22. [30]

    Bacteria in the ageing gut: did the taming of fire promote a long human lifespan? Environmental Microbiology, 20(6):1966–1987, 2018

    Antoine Danchin. Bacteria in the ageing gut: did the taming of fire promote a long human lifespan? Environmental Microbiology, 20(6):1966–1987, 2018

    1966

  23. [31]

    BioGRID: a general repository for interaction datasets

    Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, and Mike Tyers. BioGRID: a general repository for interaction datasets. Nucleic Acids Research, 34(suppl_1):D535–D539, 2006

    2006

  24. [32]

    Human ageing genomic resources: new and updated databases

    Robi Tacutu, Daniel Thornton, Emily Johnson, Arie Budovsky, Diogo Barardo, Thomas Craig, Eugene Diana, Gilad Lehmann, Dmitri Toren, Jingwei Wang, et al. Human ageing genomic resources: new and updated databases. Nucleic Acids Research, 46(D1):D1083–D1090, 2017

    2017

  25. [33]

    Cancer genome landscapes

    Bert V ogelstein, Nickolas Papadopoulos, Victor E Velculescu, Shibin Zhou, Luis A Diaz, and Kenneth W Kinzler. Cancer genome landscapes. Science, 339(6127):1546–1558, 2013

    2013

  26. [34]

    The cosmic cancer gene census: describing genetic dysfunction across all human cancers

    Zbyslaw Sondka, Sally Bamford, Charlotte G Cole, Sari A Ward, Ian Dunham, and Simon A Forbes. The cosmic cancer gene census: describing genetic dysfunction across all human cancers. Nature Reviews Cancer, page 1, 2018

    2018

  27. [35]

    A combinatorial approach to graphlet counting.Bioinformatics, 30(4):559–565, 2014

    Tomaž Hoˇcevar and Janez Demšar. A combinatorial approach to graphlet counting.Bioinformatics, 30(4):559–565, 2014

    2014

  28. [36]

    The cambridge dictionary of statistics

    Brian Everitt and Anders Skrondal. The cambridge dictionary of statistics. 2002

    2002

  29. [37]

    Temporal network alignment via GoT-W A VE.Bioinformatics, 2019

    David Aparício, Pedro Ribeiro, Tijana Milenkovi ´c, and Fernando Silva. Temporal network alignment via GoT-W A VE.Bioinformatics, 2019

    2019

  30. [38]

    Visualizing data using t-sne.Journal of Machine Learning Research, 9(Nov):2579–2605, 2008

    Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne.Journal of Machine Learning Research, 9(Nov):2579–2605, 2008

    2008

  31. [39]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learni...

    2011

  32. [40]

    Classification based upon gene expression data: bias and precision of error rates

    Ian A Wood, Peter M Visscher, and Kerrie L Mengersen. Classification based upon gene expression data: bias and precision of error rates. Bioinformatics, 23(11):1363–1370, 2007

    2007

  33. [41]

    On selection biases with prediction rules formed from gene expression data

    JX Zhu, GJ McLachlan, L Ben-Tovim Jones, and IA Wood. On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference, 138(2):374–386, 2008

    2008

  34. [42]

    Network- based protein structural classification

    Khalique Newaz, Arash Rahnama, Mahboobeh Ghalehnovi, Panos J Antsaklis, and Tijana Milenkovic. Network- based protein structural classification. arXiv preprint arXiv:1804.04725, 2018

    2018 arXiv

  35. [43]

    Improved support vector classification using pca and ica feature space modification

    Jeff Fortuna and David Capson. Improved support vector classification using pca and ica feature space modification. Pattern recognition, 37(6):1117–1129, 2004

    2004

  36. [44]

    The elements of statistical learning

    Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning. sss, 2009

    2009

  37. [45]

    Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

    Anne-Laure Boulesteix and Carolin Strobl. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMC Medical Research Methodology, 9(1):85, 2009

    2009

  38. [46]

    Reducing the probability of false positive research findings by pre-publication validation–experience with a large multiple sclerosis database

    Martin Daumer, Ulrike Held, Katja Ickstadt, Moritz Heinz, Siegfried Schach, and George Ebers. Reducing the probability of false positive research findings by pre-publication validation–experience with a large multiple sclerosis database. BMC Medical Research Methodology, 8(1):18, 2008

    2008

  39. [47]

    A measure of the impact of cv incompleteness on prediction error estimation with application to pca and normalization

    Roman Hornung, Christoph Bernau, Caroline Truntzer, Rory Wilson, Thomas Stadler, and Anne-Laure Boulesteix. A measure of the impact of cv incompleteness on prediction error estimation with application to pca and normalization. BMC Medical Research Methodology, 15(1):95, 2015

    2015

  40. [48]

    A survey on similarity measures in text mining

    MK Vijaymeena and K Kavitha. A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(2):19–28, 2016

    2016

  41. [49]

    Network propagation: a universal amplifier of genetic associations

    Lenore Cowen, Trey Ideker, Benjamin J Raphael, and Roded Sharan. Network propagation: a universal amplifier of genetic associations. Nature Reviews Genetics, 18(9):551, 2017

    2017

  42. [50]

    Improving inference of the dynamic biological network underlying aging via network propagation

    Khalique Newaz and Tijana Milenkovi´c. Improving inference of the dynamic biological network underlying aging via network propagation. arXiv preprint arXiv:1807.05637, 2020

    2020 arXiv

  43. [51]

    Hint: High-quality protein interactomes and their applications in understanding human disease

    Jishnu Das and Haiyuan Yu. Hint: High-quality protein interactomes and their applications in understanding human disease. BMC Systems Biology, 6(1):92, 2012. 19

    2012

  44. [52]

    A proteome-scale map of the human interactome network

    Thomas Rolland, Murat Ta¸ san, Benoit Charloteaux, Samuel J Pevzner, Quan Zhong, Nidhi Sahni, Song Yi, Irma Lemmens, Celia Fontanillo, Roberto Mosca, et al. A proteome-scale map of the human interactome network. Cell, 159(5):1212–1226, 2014

    2014

  45. [53]

    A reference map of the human binary protein interactome

    Katja Luck, Dae-Kyum Kim, Luke Lambourne, Kerstin Spirohn, Bridget E Begg, Wenting Bian, Ruth Brignall, Tiziana Cafarelli, Francisco J Campos-Laborie, Benoit Charloteaux, et al. A reference map of the human binary protein interactome. Nature, pages 1–7, 2020. 20

    2020

Pith tools

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