Deep and Probabilistic Models for Gene Regulatory Network Inference

Claudia Skok Gibbs

REVIEW 3 major objections 4 minor 300 references

Deep and Probabilistic Models for Gene Regulatory Network Inference

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The thesis argues that gene regulatory network inference is best split into two stages: a sequence-based prior that transfers across species and a probabilistic factorization that refines edges with calibrated uncertainty.

desk verdict A solid two-method thesis whose main real weakness is that the headline transferability claim rests on cross-species experiments that don't report homology-based leakage controls. read the letter →

arxiv 2607.16053 v1 pith:TEMT24EL submitted 2026-07-17 stat.ML cs.LGstat.APstat.ME

Claudia Skok Gibbs This is my paper · ORCID

classification stat.MLcs.LGstat.APstat.ME

keywords generegulatorynetworksprobabilisticmatrixfactorizationvariationalinferencesingle-cellRNA-seqsequencelanguagemodelstransferlearninguncertaintycalibrationtranscriptionfactors

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 thesis tries to establish that the two persistent failures of gene regulatory network inference—heuristic, confidence-free expression modeling and assay-dependent, species-specific priors—have separate fixes that work best in sequence. It presents PMF-GRN, a probabilistic matrix factorization of single-cell expression in which transcription-factor activity and TF-target interactions are latent, optimized by variational inference, so model selection is principled and every edge comes with a posterior variance that is demonstrably calibrated. It then presents GLM-Prior, a genomic language model fine-tuned to score TF-gene interaction from nucleotide sequence alone, which transfers across yeast, mouse, and human settings. Together they support a dual-stage view: a transferable sequence-derived scaffold first, then expression-based refinement with quantified uncertainty. If the thesis is right, GRN inference becomes usable in organisms and cell types where chromatin or ChIP-seq priors do not exist, and predictions come with a rank ordering of confidence for experimental follow-up.

What carries the argument

The load-bearing object is the prior-knowledge matrix that anchors factorization to named transcription factors. In PMF-GRN, the regulatory matrix is decomposed as V = A ⊙ B, where A is an interaction-existence matrix with a logistic-normal prior whose means come from external evidence; without that prior, the factorization is identifiable only up to column permutation because U V^T is permutation-invariant. Variational inference maximizes a beta-weighted ELBO, and validation AUPRC on held-out prior entries selects hyperparameters. In GLM-Prior, a transformer encoder maps concatenated TF-motif and gene sequences to a pooled embedding, and a binary classifier scores the pair; the scores assem

What would settle it

Train GLM-Prior with the TF-binding motifs replaced by length-matched random sequence while keeping labels and gene sequences fixed, then evaluate on held-out species. If AUPRC does not fall substantially, the classifier is exploiting gene-sequence or label artifacts rather than TF-gene sequence compatibility, and the sequence-prior claim fails.

Watch

Extended reading notes

Core claim

On the thesis's own terms, the central discovery is that GRN reconstruction decomposes cleanly into two complementary problems. PMF-GRN treats observed expression W as generated from latent TF activities U and a regulatory matrix V = A ⊙ B; the prior over A encodes known or hypothesized TF-gene interactions, and variational inference returns posterior means and variances for every edge. The variances are well calibrated in the sense that lower-uncertainty edges are more likely to reproduce gold-standard interactions. GLM-Prior learns to predict whether a TF and a gene interact from their concatenated sequences using a transformer-based genomic language model; a model trained on one or severa

Load-bearing premise

The load-bearing premise is that TF-gene regulatory interactions can be predicted from nucleotide sequence alone—gene sequence plus TF motif—without chromatin state, cell-type context, or cofactors; if sequence is insufficient for cross-species transfer, the GLM-Prior strategy collapses (the thesis itself acknowledges this in its background chapter).

Editorial extensions

If this is right

Practitioners can replace heuristic model selection with a single variational objective and a validation AUPRC, so different generative models become comparable on the same footing.
Inferred networks carry per-edge uncertainty that is well calibrated: low-variance edges are more accurate, which helps prioritize predictions when gold standards are incomplete.
Priors built from sequence alone remove the dependence on cell-type-specific chromatin and ChIP assays, extending GRN inference to less-characterized systems.
A model trained on one species or jointly on several transfers to other species, so regulatory knowledge from well-studied organisms can scaffold inference elsewhere.
Coupling the sequence prior with probabilistic expression-based inference yields higher accuracy than either alone across yeast, mouse, and human cell lines, supporting the dual-stage design.

Reading between the lines

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

If sequence-derived priors truly transfer across species, the same scaffold could be applied to non-model organisms and clinical samples lacking chromatin data; the thesis does not test this directly.
The three-by-three cross-comparison of priors and inferers suggests the two stages are modular—improving either stage should improve the whole pipeline—a design principle the thesis demonstrates but does not state as a general claim.
Calibrated posterior variances could double as an active-learning signal for choosing which edges to validate experimentally; the thesis stops at calibration and does not explore this use.
The synthetic-trajectory experiments show the factorization struggles with long linear cascades, hinting that explicit temporal structure is the natural next extension rather than a refutation of the approach.

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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

Worth a look if you care about single-cell GRN inference. The two methods are real, and the species-transfer story in Chapter 4 is interesting, but read the evaluation carefully before trusting the headline numbers.

What is actually new: PMF-GRN is, as far as I know, the first probabilistic matrix factorization applied to single-cell GRN inference. The variational setup gives posterior uncertainty that calibrates with accuracy in the yeast experiments, and the hyperparameter selection via held-out prior edges is a genuine improvement over heuristic model choices. Note the method is competitive with BBSR and CellOracle, not uniformly better, and it underperforms on the BEELINE long-linear synthetic, which the chapter honestly reports. The GLM-Prior chapter is the more original piece: fine-tuning Nucleotide Transformer to score TF-gene pairs from sequence alone, then feeding that as a prior into PMF-GRN. The paired prior/GRN comparison across nine combinations is a nice way to disentangle prior quality from inference quality.

Soft spots: the abstract oversells PMF-GRN. The evaluation also has a partial circularity problem: hyperparameters are tuned on held-out prior edges, then final AUPRC is computed against gold standards that share many edges with the prior, so you are partly measuring recovery of known prior interactions rather than true discovery. That does not sink the method, but it means the absolute AUPRC numbers should be read as similarity to the reference, not discovery rate. The bigger issue is the cross-species transfer results that carry the dual-stage argument. A hashFrag homology-leakage control is reported only for yeast gene and TF splits. For the mouse↔human transfer experiments in Figures 4.4–4.5, the text does not describe homology pruning against the training species. Human and mouse share extensive orthologous promoters and TF motifs, so a model could score cross-species TF-gene pairs highly by recognizing near-identical sequences without learning a generalizable regulatory code. That gap is load-bearing: if transfer performance is mostly orthology, the 'sequence-based prior transfers to less-characterized species' claim weakens considerably. The author does acknowledge in Section 2.4.4.5 that sequence alone does not encode chromatin state, cofactors, or 3D genome organization, which is honest, but the evaluation needs to quantify how much of the transfer signal is purely homology.

Both chapters were already peer-reviewed and published (Genome Biology 2024 and 2025), so the thesis itself is not brand-new results, but as a compiled dissertation it is a fair and well-written presentation. No code or data in the arXiv version, which is a real reproducibility deficiency. For peer review: I would send it out. The methods deserve referee time, and the homology-leakage question is exactly what a good referee should push on.

Referee Report

3 major / 4 minor

Summary. The thesis develops two complementary frameworks for gene regulatory network (GRN) inference. Chapter 3 presents PMF-GRN, a probabilistic matrix factorization model with variational inference, which decomposes single-cell expression into transcription factor activity and TF–gene interaction matrices, incorporates prior knowledge through the prior on the interaction matrix, performs hyperparameter selection via a held-out portion of the prior, and outputs posterior uncertainty estimates. Chapter 4 introduces GLM-Prior, a sequence-based prior constructed by fine-tuning a nucleotide transformer to classify TF–gene pairs as interacting or non-interacting, with experiments in yeast, mouse, and human, including single-species training, cross-species transfer, and multi-species training. The thesis argues for a dual-stage view in which sequence-derived priors provide a transferable scaffold and probabilistic inference refines the network with uncertainty quantification. The empirical chapters benchmark against Inferelator, SCENIC, CellOracle, and BEELINE synthetic networks.

Significance. If the claims are borne out, the work makes useful contributions: PMF-GRN decouples generative modeling from inference, provides a principled model-selection procedure, and produces uncertainty estimates that appear well-calibrated in yeast. GLM-Prior addresses a real bottleneck—constructing priors without cell-type-specific assays—and the multi-species experiments are ambitious. The thesis also ships a large amount of experimental work across organisms and synthetic benchmarks, and it is commendably explicit about limitations such as the long-linear synthetic case and the acknowledged incompleteness of gold standards. However, the current evidence does not fully support the strongest claims: the abstract's assertion that PMF-GRN is 'more accurate' than state-of-the-art methods is contradicted by the paper's own figures showing competitive rather than uniformly superior performance, and the cross-species transfer claim for GLM-Prior lacks a homology-aware leakage control for the mouse↔human experiments that are its main evidence.

major comments (3)

[§3.1 Abstract; §3.3.3, Fig. 3.2A; §3.3.6, Fig. 3.5B] The abstract states that 'PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods,' but the results do not support this uniformly. In Fig. 3.2A, PMF-GRN is competitive with BBSR and CellOracle rather than clearly better; in the 5-fold CV comparison it is similar to BBSR and StARS; and in the BEELINE long-linear synthetic networks (Fig. 3.5B) PMF-GRN is explicitly non-competitive. The wording should be tempered to 'competitive with, and often better than' and the per-dataset exceptions should be stated. This is not merely a presentational issue: the abstract is the central claim of the chapter.
[§3.3.1, Fig. 3.1C; §3.3.3, Fig. 3.2; §3.4.8] The evaluation of PMF-GRN mixes prior-recovery with true discovery in a way that is not quantified. Hyperparameters are selected by AUPRC against a held-out 20% of the prior matrix (Fig. 3.1C), and the final AUPRC is then computed against a 'gold standard.' If the gold standard shares many edges with the prior (as is likely for database- and ChIP-derived references versus motif/accessibility priors), tuning on the prior directly inflates the reported AUPRC because the model is optimized to recover prior-known edges. The manuscript does not report the overlap between the prior and the gold standard, nor performance restricted to gold-standard edges absent from the prior. Please quantify this overlap and report AUPRC on prior-absent edges, or otherwise demonstrate that the reported gains are not primarily prior-recovery.
[§4.3.3, Figs. 4.4–4.5; Supplement Fig. 4.12] The species-transfer claim for GLM-Prior depends on the mouse↔human transfer experiments, but the only homology-aware leakage control (hashFrag) is reported for yeast gene and TF splits (Supplement Fig. 4.12). No equivalent pruning or leakage analysis is described for the mouse→human and human→mouse transfer or multi-species experiments. Because human and mouse share substantial orthologous promoter and TF sequences, a model trained on mouse could score human TF–gene pairs highly merely by recognizing near-identical sequence, without learning a generalizable regulatory code. The transfer AUPRC gains in Figs. 4.4–4.5 could therefore be inflated by sequence homology. This is load-bearing for the dual-stage thesis, which depends on GLM-Prior being genuinely transferable. Please apply hashFrag-based homology pruning to the transfer and multi-species training/test splits, or otherwise quantif

minor comments (4)

[§2.4.4.3, Eq. (2.5)] Equation (2.5) is written as p(W|U,V,σ) = p(U)p(V)p(σ), which equates a conditional likelihood with prior distributions. This should be corrected to define the prior p(U,V,σ) and the likelihood separately, e.g., p(U,V,σ) = p(U)p(V)p(σ).
[Various] The text contains several typos and inconsistent spellings: 'Sacchromyces cerevisiae' (Chapter 3), 'Cell Oracle' versus 'CellOracle', 'releationships' in Fig. 2.4 caption, 'Seqence Language Models' as a section heading, and 'messanger RNA'. These should be corrected in a final pass.
[§4.3.3 and Supplement Fig. 4.12] The hashFrag homology analysis is only in the supplementary material and is not cited in the main text of Section 4.3.3 where transfer learning is described. Add a reference to this control and explicitly note its scope (yeast only) so readers are not misled about the coverage of the leakage analysis.
[§3.4.4 / §3.3.3] The term 'independent gold standard' is used without defining what 'independent' means in relation to the prior. Since the prior and gold standard may share edges, please state explicitly how the gold standard was constructed and what overlap with the prior was measured.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PMF-GRN and GLM-Prior use held-out evaluation; self-citations are not load-bearing.

full rationale

The thesis's central claims are supported by internally conducted experiments with held-out evaluation. PMF-GRN's hyperparameter selection uses a held-out portion of the prior knowledge matrix as a validation criterion, but the reported performance is evaluated against independent gold standards, and the BEELINE experiment explicitly splits ground truth so that the half used as prior is disjoint from the half used for evaluation. Negative controls with shuffled or absent priors further show that the model is not merely recapitulating its input by construction. GLM-Prior is trained on labeled TF-gene pairs and evaluated on held-out gold standards across cell lines and species; the acknowledged limitation that sequence alone does not encode all regulatory determinants (Section 2.4.4.5) is a scope caveat, not a circular reduction. The self-citations to the author's own prior work ([Skok Gibbs et al. 2022, 2024, 2025]) are cross-references to content reproduced or extended in the thesis, and no load-bearing premise depends solely on an unverified self-citation. The concern that mouse-human transfer experiments lack the hashFrag homology control reported for yeast (Supplement Figure 4.12) is a legitimate evaluation-robustness caveat, but it does not make the reported AUPRC equivalent to the training input by construction. No equation or fitted parameter is reused as its own prediction.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the domain assumptions listed above. No new physical or biological entities are postulated; TFA is a latent variable, not a new entity. Free parameters are the hyperparameters tuned to achieve the reported AUPRC.

free parameters (4)

PMF-GRN prior strength σ_a = tuned via validation AUPRC
Controls the strength of the prior over interaction matrix A; selected by hyperparameter search (Section 3.4.2).
PMF-GRN TF activity prior σ_u = tuned
Controls the prior over latent TF activities U; selected by hyperparameter search (Section 3.4.2).
β (beta) in modified ELBO = tuned
Weights the KL term in the modified ELBO (β-VAE style); selected by hyperparameter search (Section 3.4.2).
GLM-Prior class weights / negative downsampling rate = selected by one-epoch sweep
Used to handle class imbalance in TF-target interaction classification; tuned per dataset (Section 4.3.1, Figure 4.2A).

assumptions (3)

domain assumption TF-gene regulatory interactions are predictable from nucleotide sequence alone (gene sequence + TF motif), without chromatin context or cell-type-specific data.
GLM-Prior relies on this premise; the thesis itself states in Section 2.4.4.5 that sequence alone misses many regulatory determinants.
domain assumption Observed gene expression is a linear combination of latent TF activities, W ≈ U V^T with V = A ⊙ B, under Gaussian noise.
PMF-GRN's generative model (Section 3.4.1); the linear assumption may be too simple for complex regulation.
domain assumption Reference gold standards are sufficiently complete to benchmark GRN inference methods.
The thesis acknowledges gold standards are incomplete (Section 2.4.2.5); without them, AUPRC comparisons are only partially reliable.

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Cite this review

Pith. "Pith review of Deep and Probabilistic Models for Gene Regulatory Network Inference." pith.science (2026). https://pith.science/paper/TEMT24EL

@misc{pith2026260716053,
  author       = {Pith},
  title        = {Pith review of: Deep and Probabilistic Models for Gene Regulatory Network Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TEMT24EL}},
  note         = {Machine review of arXiv:2607.16053}
}

read the original abstract

Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints. Many methods couple modeling assumptions to a specific inference procedure and rely on heuristic model selection, while evaluation is constrained by incomplete reference networks and point-estimate outputs that lack uncertainty. GRN reconstruction also depends on prior knowledge to constrain TF-gene interactions, yet available priors are often assay-dependent and difficult to transfer across species and less-characterized systems. In this thesis, we develop two complementary frameworks that address these limitations. In the first, PMF-GRN casts GRN inference as a probabilistic graphical model optimized by variational inference, enabling principled model selection and uncertainty-aware edge estimates. In the second, GLM-Prior addresses the prior bottleneck by fine-tuning the pretrained Nucleotide Transformer to predict TF-target gene interactions directly from nucleotide sequence, while generalizing across yeast, mouse, and human settings. Together, PMF-GRN and GLM-Prior motivate a dual-stage view of GRN reconstruction in which sequence-derived priors provide a transferable starting scaffold and probabilistic inference refines regulatory estimates with quantified uncertainty under incomplete evaluation resources.

Figures

Figures reproduced from arXiv: 2607.16053 by the authors.

**Figure 2.1.** The central dogma of biology, illustrating the flow of genetic information from DNA to RNA via transcription and from RNA to protein via translation. Among the many types of proteins produced in the cell, transcription factors (TFs) play a central role in controlling gene regulation. TFs are DNA-binding proteins that recognize specific sequence motifs and modulate transcription by influencing the recruitment and act… view at source ↗

**Figure 2.2.** Schematic linking the central dogma to gene regulatory networks. A gene is transcribed into mRNA and translated into protein; when this protein is a transcription factor, it binds to DNA to regulate the transcription of a target gene, initiating a subsequent round of transcription and translation. This repeated feedback process is summarized as a directed graph, where nodes represent genes (or TFs) and directed edge… view at source ↗

**Figure 2.3.** Schematic overview of data modalities commonly used to reconstruct gene regulatory networks. (A) Microarray workflow: labeled cDNA hybridizes to probes on a fixed array to measure transcript abundance via fluorescence intensity. (B) Bulk RNA-seq: population-level transcriptomes are sequenced to quantify gene expression across conditions or samples. (C) Single-cell RNA-seq: RNA from individual cells is barcoded, seq… view at source ↗

Figures from the paper (25 more)

**Figure 2.4.** Figure 2.4: Schematic overview of the input and output of GRN inference algorithms. The primary input is a gene expression matrix, and optional secondary input is a prior knowledge matrix. The selected GRN inference algorithm will produce a directed acyclic graph describing pred…

**Figure 2.5.** Figure 2.5: Snapshot sampling and temporal mismatch between TF transcripts and target-gene expression. Schematic time course illustrating how TF mRNA abundance can be offset in time from the regulatory events that drive target gene transcription. At𝑡0, TF mRNA is highly expres…

**Figure 2.6.** Figure 2.6: Key challenges in gene regulatory network inference. (A) A central challenge in GRN reconstruction is determining which transcription factors (TFs) regulate which target genes. (B) Regulation is often combinatorial, with multiple TFs acting cooperatively or competi…

**Figure 2.7.** Figure 2.7: Typical pipeline for constructing prior-knowledge matrix for downstream GRN inference. (A) Open chromatin regions are identified from ATAC-seq data to define candidate regulatory DNA accessible to transcription factor (TF) binding. (B) These accessible regions are sc…

**Figure 3.1.** Figure 3.1: (A) PMF-GRN graphical model overview. Input single-cell gene expression 𝑊 is decomposed into several latent factors. Information obtained from chromatin accessibility data or genomics databases is incorporated into the prior distribution for 𝐴. (B) Input experimenta…

**Figure 3.2.** Figure 3.2: GRN inference in S. cerevisiae. (A) Consensus Network AUPR with a normal prior-knowledge matrix (N): PMF-GRN (red) performance compared to Inferelator algorithms (AMuSR in yellow, BBSR in orange, StARS in green), SCENIC (blue), and CellOracle (purple). Dashed line re…

**Figure 3.3.** Figure 3.3: (A) GRNs inferred by downsampling S. cerevisiae expression data. (B) Hyperparameter search performed on 4 different ratios of cross-validation. Dots represent validation AUPRC from hyperparameter search during cross-validation, triangle represents AUPRC from a GRN l…

**Figure 3.4.** Figure 3.4: GRN and TFA inference in PBMC. (A) UMAP projection of predicted TFA for each annotated PBMC cell type. (B) Predicted IRF2 TFA demonstrates high activity in NK and CD8 T cells. (C) Heat-map dot-plot depicting TFA of selected immune TFs across annotated PBMC cell types…

**Figure 3.5.** Figure 3.5: PMF-GRN performance on BEELINE synthetic GRN data (A) PMF-GRN inference performance with half of the ground truth provided as prior network information and the remaining half provided as a gold standard for evaluation. Dashed lines are the expected baseline of a ra…

**Figure 4.1.** Figure 4.1: Schematic of the dual-stage training pipeline. (A) GLM-Prior is trained on concatenated TF binding motifs and gene sequences labeled as interacting or non-interacting. Input data is split into training and validation sets, where the training set is downsampled and p…

**Figure 4.2.** Figure 4.2: GLM-Prior model performance across yeast, hESC, and HepG2 cell lines. (A) One-epoch hyperparameter sweep over class-weights and negative-class downsampling rates, evaluated by F1-score. (B) Validation metrics during 10 epoch training using the best hyperparameter con…

**Figure 4.3.** Figure 4.3: GLM-Prior model performance across mESC, mDC, and mHSC cell lines. (A) One-epoch hyperparameter sweep over class-weights and negative-class downsampling rates, evaluated by F1-score. (B) Validation metrics during 10 epoch training using the best hyperparameter config…

**Figure 4.4.** Figure 4.4: Transfer learning and multi-species training with GLM-Prior. (A) Schematic and AUPRC results of the species-transfer learning setup, showing three configurations: (𝑖) a model trained jointly on human and mouse data evaluated on yeast, (𝑖𝑖) a mouse-only model evaluate…

**Figure 4.5.** Figure 4.5: Transfer learning and multi-species training with GLM-Prior continued. (A) Comparison of training composition (number of genes and TFs) for single-species, species-transfer, and multi-species models. (B) Class composition (positive and negative labels) in the corresp…

**Figure 4.6.** Figure 4.6: Integration of GLM-Prior with PMF-GRN for downstream GRN inference. (A) AUPRC comparison of GLM-Prior alone versus integrated with PMF-GRN across six cell line contexts. (B) Edge flip analysis quantifying changes made to the prior knowledge interaction matrix during…

**Figure 4.7.** Figure 4.7: Comparison of prior construction strategies across species. (A) AUPRC of GLM-Prior, Inferelator-Prior and CellOracle Prior across six cell lines, with chance performance (gray) provided for each context. (B) Normalized AUPRC values with the best-performing prior (gre…

**Figure 4.8.** Figure 4.8: Paired prior-GRN strategies reveal prior dependent gains from expression-based GRN inference across six cell lines. (A) AUPRC of each prior (GLM-Prior, Inferelator-Prior, and CellOracle’s prior) and its corresponding GRN (PMF-GRN, Inferelator, CellOracle) across six…

**Figure 4.9.** Figure 4.9: Paired prior-GRN strategies reveal prior dependent gains from expression-based GRN inference across six cell lines continued. (A) Normalized AUPRC performance for priors and GRNs in each cell line. Green boxes highlight the best prior and blue boxes highlight the be…

**Figure 4.10.** Figure 4.10: Cross-method comparison disentangles prior and GRN inference contributions. (A) AUPRC for all nine combinations of prior and GRN inference methods across six cell lines. Gray dashed lines indicate chance performance, while light blue, light green, and light pink das…

**Figure 4.11.** Figure 4.11: Cross-method comparison disentangles prior and GRN inference contributions continued. (A) Normalized AUPRC of all nine combinations of prior and GRN for directly comparing across method and cell line. Blue boxes indicate highest GRN performance per cell line. Simila…

**Figure 4.12.** Figure 4.12: Sequence-homology analysis of yeast gene and TF splits using hashFrag. (A) [PITH_FULL_IMAGE:figures/full_fig_p180_4_12.png]

**Figure 4.13.** Figure 4.13: Performance Metrics. Additional performance metrics for GLM-Prior and PMF-GRN across six cell lines. 164 [PITH_FULL_IMAGE:figures/full_fig_p188_4_13.png]

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Pith tools

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