respond to related email inquiries? Hosting of the code and data is not an issue for authors due to availability of archival data repositories such as Zenodo. However, actively maintaining the code and data is an issue, and this is directly related to whether and how long after the project completion the funding by the federal agencies and others might be available for this purpose.

Complete transparency in all decisions (from graph construction to analysis) is crucial. Workflow management systems, such as Nextflow (Di Tommaso et al. 2017) and Snakemake (Köster and Rahmann 2012), can enable rapid prototyping and deployment of computational workflows by combining software packages and various tools. Clear documentation, open-source sharing of code and algorithms, and making raw and processed data available can ensure that results are not just a one-off finding but can be consistently reproduced and built upon by the broader scientific community.

## On missing data

**Network completeness and interaction causality.** Much of network biology relies on aging technologies with notable limitations. Focusing on physical PPIs, biotechnologies such as yeast two-hybrid systems (Fields and Song 1989), cross-linking mass-spectrometry (Piersimoni et al. 2022), and structural determination of protein complexes (Jacobsen 2007, Rhodes 2010, Saibil 2022) have collectively generated systems-level data that have led to critical methodological advances in network biology. Of course, these efforts to obtain the physical interactome have been complemented by valuable data collection and network inference efforts related to systems-level correlation networks. However, as computational methods are now maturing, the data are starting to lag. High-resolution, high-throughput data-generating technologies, capable of directly identifying pathways and order of molecular events in various experimental and clinical contexts, are the next frontier for deeper understanding of molecular systems.

There is a need to expand from physical and correlation networks toward causal relationships (Belyaeva et al. 2021) or simulatable kinetic models (Karr et al. 2012). For this, biotechnologies for data collection need to be improved to allow for higher-quality data to build better causal networks and more complete networks. This will also require the development of new (categories of) approaches that can handle the captured causality. Even if/when we have high-quality causal networks and efficient and accurate methods for their analysis, will this suffice to understand biochemical mechanisms? When one knows biochemical mechanisms, one can infer causality. However, causality might not necessarily allow for fully understanding biochemical mechanisms.

**Algorithmic research to guide data generation efforts.** It will likely be beneficial to integrate multi-omic network data with BKGs to offer precise and targeted treatments for rare diseases (Alsentzer et al. 2022). Such network data with richer semantics will more directly help suggest biological hypotheses (Sanghvi et al. 2013, Wang et al. 2023a) or support iterative data generation and analyses through active learning (Sverchkov and Craven 2017, Zhang et al. 2023a). Informing laboratory experiments using predictions from computational studies could be a path forward to build more complete and accurate data, which could lead to developing new, more advanced network analysis methods to further inform and improve laboratory experiments.

How network biology (primarily algorithmic research) can best support the collection and analysis of multimodal data is quite an important question, especially when collecting multimodal data for the same individuals, including building personalized (i.e. individual-specific) networks. An answer here could be to first figure out what question will be asked in which task/application and then design a data collection strategy. One might want to define optimal datasets. Or, one might want to find unifying factors within data modalities; this is precisely why there is a need for multimodal data for the same individuals, at least some of the data/individuals. This might require systematic, comprehensive, and well-funded consortia efforts. Perhaps algorithmic approaches such as active learning can help prioritize what data should be collected, e.g. from specific populations or about particular biological functions. As success in experimentally collecting or computationally inferring various types of biological networks continues to improve, research efforts likely should shift towards obtaining a predictive understanding of personalized networks. Moreover, even within a single individual, molecular networks vary across tissues and cell types, posing additional challenges in defining an individual-specific network.

**Network dynamics.** Another data component that is currently missing or is very scarce is network dynamics. Various types of time-dependent perturbation data could help infer dynamic biological networks. Examples of tasks/applications that have benefited from dynamic network analysis in biology are as follows.

One example is the task of network alignment: unlike traditional network alignment that has compared static networks (Section 3), recently, the problem of aligning dynamic networks has been defined, and several algorithms have been proposed for solving the newly defined problem (Vijayan et al. 2017, Vijayan and Milenković 2018b, Aparicio et al. 2019). The challenge here is the lack of experimentally obtained dynamic biological network data, which is why such methods have been evaluated on synthetic networks, computationally inferred dynamic biological networks, or dynamic networks from other domains (Vijayan et al. 2017, Vijayan and Milenković 2018b, Aparicio et al. 2019).

Another example is a recent network-based study of the dynamics of the protein folding process (Newaz et al. 2022). A key challenge is the lack of large-scale data on protein folding intermediates, i.e. 3D conformations of a protein as it undergoes folding to attain its native structure. Experimental data of this type are lacking even on the small scale (Newaz et al. 2022). Traditional computational, simulation-based studies, as well as the recent network-based effort (Newaz et al. 2022), all approximate the folding intermediates of a protein from the protein's final (or native) 3D structure. Obtaining the actual protein folding intermediates experimentally is unlikely to happen any time soon, especially at a large scale, so computational efforts will be needed. With recent breakthroughs in protein structure prediction, e.g. AlphaFold (Jumper et al. 2021), this need represents an excellent opportunity for computational research to help obtain, model, and analyze the resulting dynamic data.

A further example is a dynamic network analysis of the aging process, i.e. predicting new aging-related genes from a dynamic aging-specific PPI network (Section 2). Here, a key challenge is that shockingly, using newer aging-related gene expression and PPI network data obtained via newer and thus higher-quality biotechnologies to infer a dynamic aging-
specific network does not yield more accurate aging-related gene predictions than using *older* data of the same type from over a decade ago when dynamic network analyses of aging were pioneered (Li et al. 2022c). It was also observed in a different study on active module identification that using newer network data typically did not lead to more biologically meaningful results (Lazareva et al. 2021). Going back to aging, it remains unclear whether the issue is with gene expression data, PPI network data, methods for integrating the two to computationally infer a dynamic aging-specific network, network methods used for feature extraction from the aging-specific network, ground-truth data on which genes are aging- versus nonaging-related, or something else entirely (Li et al. 2022c).

As our final example, we discuss quantitative and qualitative mathematical modeling of network dynamics from the systems biology perspective (Kestler et al. 2008, Le Noveré 2015). Quantitative formalisms provide a precise description of the evolution of the system, including its temporal aspects; they are strongly dependent on the availability and precision of the required parameters. At the other end of the spectrum, qualitative (logic) frameworks have the advantage to be simpler, with no requirement for quantitative parameters, allowing analytical analyses. Logical models allow coarse-grained descriptions of the properties of the biological network and bring out key actors and mechanisms controlling the dynamics of the system (Maheshwari and Albert 2017). Recent efforts use -omics data, including single-cell transcriptomes, to construct or contextualize Boolean models (Schwab et al. 2021, Montagud et al. 2022, Hérault et al. 2023).

**Towards inclusive and equitable precision medicine.** Progress in computational (including network) biology and biomedicine has been hindered by a lack of -omics data encompassing vast human diversity (Cruz et al. 2023). Underrepresentation of human genetic diversity has drastically weakened the biological discoveries that would benefit all populations, leading to health disparities. The traditional one-size-fits-all healthcare model meant for a “typical” patient may not work well for everyone. In response, the National Institutes of Health has aimed to invite one million people across the USA to help build one of the most diverse health databases in history, welcoming participants from all backgrounds through the “All of Us” program (https://allofus.nih.gov/). Inclusivity is at the core of the program: participants are diverse in terms of their races, ethnicities, age groups, regions of the country, gender identity, sexual orientation, socioeconomic status, education, disability, and health status. The data collected through the program is expected to lead to discoveries on how our biology, environment, and lifestyle affect our health. Unlike traditional research that has focused on a particular disease or group of people, this program aims to build a diverse database that can inform thousands of studies on a variety of health conditions. Availability of inclusive and diverse -omics data, design of research studies that intentionally and carefully account for such data, and development of computational methods and evaluation frameworks that handle such data in a fair and unbiased manner will be critical for advancing computational biology and biomedicine for all populations and reaching health equity.

Beyond the issue of underrepresentation, certain populations are intrinsically limited in size, such as rare diseases, which are inherently limited in clinical cases (Banerjee et al. 2023). Studying a substantial fraction of a small population may still result in data that do not yield health outcomes

comparable to those from larger populations. In such scenarios, amassing more data may not be feasible, leading to small-sample datasets that can lack statistical power and magnify the susceptibility of computational models to misinterpretation and unstable performance. Network analysis techniques can play a pivotal role in addressing this challenge. Techniques such as few-shot machine learning (Alsentzer et al. 2022) and domain adaptation (He et al. 2023) for network methods are instrumental in enabling computational models to learn patterns from small datasets and generalize to newly acquired data. Such models can adapt and generalize across diverse populations, thereby enhancing the robustness and applicability of health outcomes derived from datasets with small numbers of samples.

## Other major future research advancements

**The interface between network biology and LLMs.** LLMs, such as ChatGPT and GPT-4, create opportunities to unify natural language processing and knowledge graph reasoning (Fatemi et al. 2023, Pan et al. 2024), owing to their wide-ranging applicability. Nevertheless, LLMs often serve as black-box models, presenting limitations in comprehensively capturing and accessing factual knowledge. In contrast, BKGs are structured knowledge models that systematically store extensive factual information. BKGs have the potential to enhance LLMs by providing external knowledge that aids in inference and bolstering interpretability. However, constructing BKGs is intricate and dynamic, posing challenges to existing methods in generating novel facts and representing previously unseen knowledge. Thus, an approach integrating LLMs and BKGs could emerge as a valuable strategy, harnessing their strengths in tandem (Pan et al. 2024).

The potential synergies between traditional text and structured knowledge graphs are becoming increasingly evident. Language model pretraining has proven invaluable in extracting knowledge from text corpora to bolster various downstream tasks. Yet, these models predominantly focus on single documents, often overlooking interdocument dependencies or broader knowledge scopes. Recent advances (Yasunaga et al. 2022b, McDermott et al. 2023) address this limitation by conceptualizing text corpora as interconnected document graphs. By placing linked documents in shared contexts and adopting self-supervised objectives combining masked language modeling and document relation prediction, such methods can achieve considerable progress in tasks like multi-hop reasoning and few-shot question answering. On a parallel front, while text-based language models have garnered substantial attention, knowledge graphs can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning. In an emerging line of inquiry, studies (Yasunaga et al. 2022a) explore self-supervised paradigms to construct a unified foundation model, intertwining text and knowledge graphs. These approaches pretrain models by unifying two self-supervised reasoning tasks, masked language modeling, and link prediction, marking an exciting direction for future advancements in network biology.

LLMs, traditionally associated with the processing of natural language, possess a flexibility that extends their utility beyond text data (Luo et al. 2022a). The underlying architectures, especially transformer-based designs like BERT and GPT variants, can be adapted to learn from any sequential data. In biology, this adaptability implies that LLMs can be
trained on biological sequences, such as DNA, RNA, and pro-
teins (Rao et al. 2019, Xu et al. 2022, Lin et al. 2023). Rather
than processing words or sentences, these models can assimi-
late nucleotide or amino acid sequences, thereby capturing in-
tricate patterns and dependencies in genomic and proteomic
data (Meier et al. 2021, Dauparas et al. 2022, Lin et al. 2023,
McDermott et al. 2023). These cross-disciplinary advances in
LLMs highlight their potential to advance the frontiers of com-
putational biology. In addition to large sequence-based pre-
trained models like LLMs, an emerging area of structure-based
pretrained models is concerned with generating new network
structures, such as protein and small molecule networks
(Townshend et al. 2021, Rodrigues and Ascher 2022, Wang
et al. 2022b, Bennett et al. 2023, Gainza et al. 2023).
Interpretability.

**Interpretability.** Interpretability in network biology involves elucidating mechanisms of disease and health, such as tumor growth and immune responses. However, deep graph learning models are black-box systems with limited immediate interpretability as they produce outputs through a series of complex, nonlinear transformations of input data points. This poses challenges in domains where clear insights are imperative. For instance, while dimensionality reduction techniques and graph representation learning algorithms produce compact latent feature representations of high-dimensional data and graphs, they often sacrifice the interpretability of the features they produce. Conversely, graph-theoretic signatures, which capture network motifs, graphlets, or other substructures, can amplify understanding of networks by identifying relevant structural patterns.

Future research directions in interpretability must focus on
integrating domain-specific knowledge into model training
and evaluation. By directly incorporating biological constraints
and prior knowledge into model architectures, we can enhance
interpretability without compromising predictive performance.
Additionally, developing explainable techniques tailored ex-
plicitly for network biology is crucial. Exploring hybrid models
combining interpretable statistical models with deep learning
approaches is another promising avenue. Such models can le-
verage the strengths of both types to produce interpretable and
accurate predictions. Likewise, creating advanced visualization
tools that effectively convey complex model outputs and bio-
logical insights to researchers and clinicians is essential. These
tools should be intuitive and enable interactive exploration of
model predictions and features.

**Reproducibility.** Reproducibility in network biology research is a multifaceted challenge due to several reasons. (i) Graph construction: How a graph is constructed can drastically impact the insights drawn from it. For example, consider the problem of inferring an association PPI network. The decision to include only direct interactions versus both direct and indirect interactions can lead to vastly different network topologies. Choosing a threshold to determine an edge (e.g. a particular strength of interaction or confidence level) can also significantly alter the graph. (ii) Edge definitions: What constitutes an edge can be subjective and is often based on the specific context. In a gene co-expression network, for instance, the definition of an edge might be based on a particular correlation coefficient threshold. A slight variation in this threshold can lead to including or excluding numerous interactions, thus changing the network's structure and potentially its inferred properties. (iii) Latent embeddings: Graph-based machine learning methods used to compute embeddings can have a significant effect on the results.

Different embedding techniques capture different types of
structural and feature-based information, leading to varia-
tions in tasks like node classification or link prediction.
(iv) Dynamic nature of biological networks: Biological sys-
tems are inherently dynamic. A PPI network at one point in
time or under one set of conditions might differ from the net-
work under another state. Thus, reproducing results requires
the same methodology and the same or equivalent biological
conditions. (v) Finally, graph sampling: In many cases, a sub-
graph or sample is taken due to the massive size of networks
or computational constraints. The method and randomness
inherent in this sampling can lead to nonreproducible results
if not carefully controlled.

**Towards wide adoption and translation of algorithmic innovation into practical and societal impact.** The recommended method evaluation and data generation improvements discussed above are needed not just for method developers—typically, computational scientists—to be able to properly evaluate their new approaches against existing ones, but even more importantly, for adoption by end users—experimental scientists and in the long run, clinicians, healthcare workers, and patients (Section 8 comments more on this topic, including training needed for noncomputational folks to use network approaches). The disconnect between computational and experimental scientists, even those dedicated to the common scientific goals (Ramola et al. 2022), suggests that efforts are necessary to overcome both technical and social challenges in interdisciplinary research fields. Computational scientists might need to consider not only traditionally algorithmic evaluation measures, such as precision, recall, and other performance criteria, but also measures that evaluate the utility and feasibility of integrating methods into scientific and clinical workflows (Huang et al. 2022, 2023a). Additionally, computational scientists are primarily incentivized to develop new algorithms and prototype software. In contrast, experimental and clinical scientists expect tools that are robust, trustworthy, and exhibit few glitches in practice. Authoritative evaluations, carried out by independent and interdisciplinary researchers on tasks directly relevant to downstream applications, are essential (Marbach et al. 2012a, Choobdar et al. 2019). Rapid and broad dissemination of these evaluations, recommendations, and guidelines for best practices should be prioritized in network biology.

**Major milestones in network biology.** The pinnacle of success for network biology would likely be a comprehensive and dynamic understanding of the entire cellular or organis-mal interactome across different conditions and life stages. This would include PPIs, gene regulation, metabolic path-ways, cell signaling, and more. We can imagine a complete map of every biological interaction in an organism, from the level of genes and molecules up to tissues and organs, with the ability to zoom in on details and see dynamic changes over time or under different conditions. Another significant milestone would be the seamless integration of network biol-ogy with other disciplines to provide a holistic understanding of life. This means connecting the molecular interactome with tissue-level networks, organ systems, and interorganis-mal interactions, such as those seen in symbiosis or ecosys-tems. From a practical standpoint, a significant success measure would be the application of network biology insights to develop novel and more effective therapeutic interventions. This could mean identifying critical network nodes or inter-actions to target diseases, leading to innovative treatments.
Drawing parallels from the reference human genome, the equivalent for network biology could be a reference interac-tome—a standardized and comprehensive map of all known bi-ological interactions within a human cell. This would serve as a baseline for studying disease, development, aging, and other bi-ological processes. Any deviations from this reference in specific cell types, conditions, or diseases could be studied in detail.

Just as AlphaFold (Jumper et al. 2021) has made waves in predicting protein structures, a comparable success in net-work biology might be the development of tools that can ac-curately predict the emergent properties of a biological system from its underlying network. Given a set of interac-tions, this would mean the tool could foresee the system's re-sponse to a drug, its behavior under certain conditions, or its evolution over time.

## 8. Additional discussion on scientific communities, education, and diversity

The question of who are network biologists or computational biologists is hard. Ideally, a computational biologist would have the interest and knowledge to both develop core compu-tational methods and understand fundamental biological mechanisms. That raises the question of how to properly train more of such researchers to advance computational bi-ology, including its subarea of network biology that models and analyzes biological systems as networks. For example, based on the personal experience of some of the authors of this article, in a network biology course, computationally fo-cused students might enjoy computational but not biological aspects (e.g. in a general network science course, students typ-ically choose a nonbiology domain to work on, such as techno-logical or social networks). In contrast, biology students might enjoy biological but not computational aspects. So, efforts might be needed to convince students to be genuinely excited about both developing computational approaches and under-standing biological mechanisms. Systematically identifying and addressing gaps in current computational biology training pro-grams or starting new interdisciplinary training programs might be needed, along with appropriate support and resources from funding agencies.

Some of these gaps are as follows. An essential part of effi-cient training would be to have robust, well-known, and trustworthy software tools that are readily available and easy to use, especially by those who are not proficient in comput-ing; clearly, both developing and sustaining such software requires resources. Similar holds for building and making available datasets easily accessible by people who are not pro-ficient in biology to help them get involved easily. Another important part would be exposing students to interdisciplin-ary collaborative teams to train them to work together on the same research questions with scientists from different disciplines.

Another vital part of training relates to hiring and promot-ing computational biology faculty who would offer the train-ing. A challenge here, based on the personal experience of some of the authors of this article, seems to be as follows. When hiring a computational biologist in a traditional com-putationally focused department (e.g. computer science, ap-plied mathematics, statistics, or physics), someone who is more trained in biology may be viewed as not enough of a computational scientist, even when they are proficient in us-ing existing computational methods to uncover new

biological knowledge and possibly also at least occasionally develop new computational methods for studying biological systems. Similarly, in a traditional biology-focused depart-ment, a more computationally trained person may be viewed as not enough of a biological scientist, even when they evalu-ate their new computational methods on biological data and possibly at least occasionally yield new knowledge about bio-logical systems. Yet, both kinds of candidates can be great for both department types. Hence, hiring and promotion groups might need to think differently about interdisciplinary com-putational biology research. This is especially true in depart-ments where these groups do not have computational biologists or where there are no specific, interdisciplinary departments like biomedical data science or computa-tional biology.

There exists an additional challenge even when focusing on computationally oriented researchers within computational biology. Scientific communities that could benefit (from) the field of network biology include graph theory, network sci-ence, data mining, machine learning, and artificial intelli-gence. These communities often use different terminology for the same concepts (e.g. network alignment versus graph matching or graph clustering versus network community de-tection). Distinct scientific communities may all analyze bio-logical network data, or address identical computational challenges across various application domains, such as bio-logical versus social networks. However, they often do not at-tend the same research forums. For instance, attendees of the prominent computational biology conference, Intelligent Systems for Molecular Biology (ISMB), might not necessarily participate in data mining conferences like Knowledge Discovery and Data Mining (KDD) or artificial intelligence conferences such as Neural Information Processing Systems (NeurIPS), and vice versa. Consequently, advancements in one domain might remain obscure in another. Organizing sci-entific symposia to convene computational scientists from traditionally distinct network biology communities, focusing on universally relevant topics, could help bridge this gap.

The above discussion items can be seen as diversity-focused, be it diversity in one's training and skills or scientific communities they belong to (Nielsen et al. 2018). Many other aspects of diversity exist in science, and we focus on some of them here. The International Society for Computational Biology (ISCB) is a globally recognized entity advocating for and advancing scholarship, research, training, outreach, and inclusive community building in computational biology and its professions. This is why we rely on ISCB's demographic statistics to represent the current state in the computational biology field. According to a demographic survey of the ISCB membership, whose results are publicly available in the 2022/2023 ISCB Equity, Diversity, and Inclusion (EDI) report (https://www.iscb.org/edi-resources), among those who responded, 32.8% indicated "female," 60% indicated "male," 0.4% indicated "non-binary," and 6.8% indicated "prefer not to declare." Regarding ethnic origin, in the same report, 53% of those who responded with anything but "prefer not to declare" indicated a non-European descent. Some additional EDI statistics are as follows. At the time of the 2020/2021 ISCB EDI report (the latest report that offered this type of information), 41% of the ISCB Board of Directors were female, and 57% of the Executive Committee (elected officers) were female; 61% of selected keynote speakers at the Intelligent Systems for Molecular Biology (ISMB),
ISCB's flagship and most prestigious conference, were female since 2016. Regarding ISCB awards, fellows election, and other honors, the final selection shows a good gender balance that reflects the membership. However, during the nomination stage, in 2022/2023, for the innovator award, senior scientist award, and fellows election, 22%, 28%, and 25% of the nominees were female, respectively, compared to 32.8% of the entire ISCB membership being female. ISCB does not have such data yet on ethnicity.

Enhancing awareness and mitigating biases when nominating candidates for honors or inviting candidates as conference speakers is a pathway to improving diversity in the computational biology field. Another more ambitious goal is to achieve diversity statistics in the field that mirror those of the general population. This should be accomplished for all undergraduate students, graduate students, postdoctoral fellows, and faculty (across various ranks), not only by addressing the “leaky pipeline” issue (Alper 1993, Sarraju et al. 2023), but also by identifying and eliminating institutional barriers to establish an inclusive support infrastructure (Stevens et al. 2021). This might only be achievable over a longer period. Also, biology-focused subfields of computational biology are currently more gender-diverse than its computationally focused subfields. Thus, diversity in computational biology might be more readily achieved by recruiting trainees from biology-focused subfields and equipping them with the requisite computational skills rather than the reverse. However, sourcing from computational subfields remains essential. Yet, disciplines like computer science, mathematics, and physics can act as gatekeepers and entering these fields without the appropriate background can be challenging (Torbey et al. 2020, Mervis 2022). Because innovative concepts can emerge from diverse sources and all individuals, it is imperative to eliminate gatekeeping barriers.

Additional diversity-related challenges include the need to recognize and mitigate potential implicit biases; limited access to registration and travel funds to conferences based on their locations, especially for those in middle and low-income countries; current lack of ethnicity data to evaluate diversity efforts of computational biology conferences and communities, including ISCB; empirical research into equity in science, etc. Systematic and properly funded initiatives by universities and professional societies are necessary to achieve this. And so are individual efforts by the members of the scientific community. Everyone should be responsible for contributing to joint diversity efforts for the field to make significant and sufficient progress.

## Acknowledgements

This work has been initialized at the Workshop on Future Directions in Network Biology held at the University of Notre Dame during 12–14 June 2022. This targeted meeting brought together 39 active researchers in various aspects of network biology to present and discuss a short- and long-term vision for computational research in this field. 31 of the workshop participants attended the meeting in person. Due to difficulties with international travel related to the COVID-19 pandemic, all in-person workshop participants were from institutions in the USA. To draw on a combination of distinct ideas and experiences, when inviting participants, an effort was made to balance diversity among the attendees along multiple axes, including seniority (full, associate, or assistant

professors, postdocs, and PhD students), affiliation (representation from academia, industry, and government), and gender (42% of the in-person participants were female). The workshop participants presented their views of important research directions, open problems, and challenges that would propel computational and algorithmic advances in network biology. Presentation slides for the scientific sessions at the workshop are linked to the workshop website (https://www3.nd.edu/~tmilenko/NetworkBiologyWorkshop/), and videos of the presentations are publicly available on YouTube (https://www.youtube.com/ playlist?list=PLy8BJXti_TvYaL7frFJz2mf38e8o0NaFN). Thanks to Siyu Yang, a Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame, for carrying out the literature search on network-of-networks analysis.

## Author contributions

Marinka Zitnik (Conceptualization [lead], Investigation [lead], Supervision [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]), Michelle M. Li (Conceptualization [equal], Investigation [equal], Visualization [lead], Writing—original draft [equal], Writing—review & editing [equal]), Aydin Wells (Conceptualization [equal], Investigation [equal], Visualization [lead], Writing—original draft [equal], Writing—review & editing [equal]), Kimberly Glass (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Deisy Morselli Gysi (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Arjun Krishnan (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), T.M. Murali (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Predrag Radivojac (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Sushmita Roy (Conceptualization [equal], Investigation [equal], Supervision [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Anais Baudot (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Serdar Bozdag (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Danny Z. Chen (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Lenore Cowen (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Kapil Devkota (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Anthony Gitter (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft
[supporting], Writing—review & editing [supporting]), Sara Gosline (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Pengfei Gu (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Pietro H. Guzzi (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Heng Huang (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Meng Jiang (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Ziynet Nesibe Kesimoglu (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Mehmet Koyuturk (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Jian Ma (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Alexander R. Pico (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Nataša Pržulj (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Benjamin J. Raphael (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Anna Ritz (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Roded Sharan (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Yang Shen (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Mona Singh (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Donna K. Slonim (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Hanghang Tong (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Xinan Holly Yang (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), Byung-Jun Yoon (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft

[supporting], Writing—review & editing [supporting]), Haiyuan Yu (Conceptualization [supporting], Investigation [supporting], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), and Tijana Milenković (Conceptualization [lead], Funding acquisition [lead], Investigation [lead], Project administration [lead], Supervision [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]).

## Conflict of interest

In Section 8, we rely on ISCB's diversity statistics. These statistics are publicly available, and so there is no conflict of interest. Yet, to remedy any potential perceived conflict of interest, we declare that Predrag Radivojac is the President of ISCB and currently serves on the Board of Directors of ISCB. In addition, Tijana Milenković currently serves on the ISCB Board of Directors and the ISCB EDI Committee. The remaining authors have no conflicts of interest to declare.

## Funding

The 2022 Workshop on Future Directions in Network Biology that initiated this work was supported by the U.S. National Science Foundation [CCF-1941447]. Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy [Contract Nos. DE-AC05 to 76RLO 1830]. The work of Teresa M. Przytycka was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health [LM200887-16].

## Data availability

Not applicable.

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