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Network Inference in Systems Biology: Recent Developments, Challenges, and Applications

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Michael M. Saint-Antoine, Abhyudai Singh

Molecular Networks · Submitted 11 Nov 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect.

how it is stated · asserted 1.00 read the record →

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arXiv ID1911.04046 (version 1)
Pith IDNYKMKTYW, Pith's permanent record of this paper and its review
CitationAbhyudai Singh, Michael M. Saint-Antoine. "Network Inference in Systems Biology: Recent Developments, Challenges, and Applications." arXiv:1911.04046 (2019). https://pith.science/paper/NYKMKTYW
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,296 input tokens.

What the abstract itself states, as probabilities: claims first 0.02 · states limitation 0.21 · human participants 0.12 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.07 · mentions code or data availability 0.13.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. background 1.00 0.04 0.05 asserted 0.99
s1 Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. background 0.67 0.05 0.13 asserted 1.00
s2 In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. contribution 0.99 0.33 0.07 asserted 0.96
s3 We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. contribution 0.84 0.12 0.07 asserted 0.95
s4 Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses. contribution 0.96 0.47 0.08 asserted 0.91

What kind of paper the classifier reports this is: review 1.00 · evidence from literature 1.00.

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not examined
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  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.

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