Multi-disciplinary Insights

What Multi-disciplinary Insights does

Multi-disciplinary Insights is a privately held, founder-led scientific consulting firm headquartered at 237 Kearny St #268 in San Francisco, operating as Multi-disciplinary Insights, LLC. The firm builds computational tools and analytical services that integrate techniques from statistical mechanics of strongly interacting systems with machine learning, applying them to high-throughput biological data and bibliometric information across academic, healthcare, and industrial research settings.

Its productized offerings center on the Physics-inspired Biomarker Discovery Toolbox, which performs variable selection on genomic and proteomic datasets for medical diagnosis and prognosis (with documented benchmark performance in which 2-gene signatures outperformed published 8-gene signatures on peripheral-blood lung cancer microarray data), and the Map of Science Visualization, a bibliometric network rendered with sigma.js covering 222 ISI journal subject categories. The firm also offers Network Analysis Services covering social networks, gene regulatory networks, metadata mapping, and complex systems visualization. The website lists adjacent research areas including proteomic biomarkers of breast cancer prognosis, biocompatibility of molecular monolayers, water effects in drug-target interactions, and nanoscale force computation.

The business operates as a professional services / consulting model on quote-based engagements, with primary customers in healthcare and biomedical research institutions and secondary segments in scientometrics organizations and pharmaceutical companies. Distribution is direct-inquiry via the website, and go-to-market relies on academic-style content and interactive web visualizations rather than conventional B2B sales. No funding rounds, partnerships, named customers, mobile apps, or APIs are disclosed; the company is led solely by founder and Managing Director Dr. Pavel Paramonov.

Name

Multi-disciplinary Insights

Legal name

Multi-disciplinary Insights, LLC

Website

https://multi-disciplinary.com

Company type

Private

Founded year

2010

Operating status

Operating

Headcount range

1–10 employees

Short description

Multi-disciplinary Insights is a San Francisco-based scientific consulting firm that develops physics-inspired computational tools and machine learning approaches for biomarker discovery, bibliometric network analysis, and complex systems visualization, serving biomedical researchers, pharmaceutical companies, and scientometrics organizations.

Ownership category

akta.pro rank

Product category

Computational biology consulting

NAICS

Research and Development in Biotechnology (except Nanobiotechnology) (541714), Other Scientific and Technical Consulting Services (54169)

SIC

Services-Commercial Physical & Biological Research (8731)

akta.pro primary industry

Bioinformatics & Multi-omics Analysis Software (HLAGAJAD)

akta.pro secondary industries

Biomarker Discovery & Validation (omics, assay feasibility, clinical validation) (HLAAAMAA), Knowledge Management & Scientific Collaboration / Literature Intelligence (HLAGAJAN), AI/ML Platforms for Drug Discovery & Experiment Optimization (HLAGAJAO)

Keywords

• Biomarker discovery
• Computational biology
• Network analysis
• Scientometrics consulting
• Variable selection

Headquarters

HQ city

San Francisco

HQ country

United States

HQ region

North America

Offices1 record

Markets served

GTM type

B2B

Offering type

Services

Cost components

Personnel, Technology or R&D, Operations, Marketing or Sales, Infrastructure

Revenue model

1. Research and consulting services: Custom research projects and consulting engagements for biomarker search in high-throughput biological datasets, pharmaceutical target contexts, and scientific mapping applications.

Go-to-market motion1 record

Distribution channels1 record

Marketing channels1 record

Core offering

Multi-disciplinary Insights develops computational tools and delivers research and consulting services centered on a physics-inspired biomarker discovery methodology and bibliometric network analysis. The company's offerings combine statistical mechanics of strongly interacting systems with machine learning to identify minimal predictive variable sets from high-throughput biological datasets and to map the structure of science across journal subject categories.

Product overview

Multi-disciplinary Insights offers a suite of research and analytical services centered around two core products: the Physics-inspired Biomarker Discovery Toolbox for selecting predictive variables from high-throughput biological datasets, and the Map of Science Visualization for bibliometric network analysis. These tools apply computational physics-inspired approaches combined with machine learning to address challenges in biomarker identification, genomics, and scientific structure mapping. The company integrates these methodologies to serve applications in cancer detection, pharmaceutical research, and scientometrics.

Differentiator

Problem solved

Functional benefit

Products and services

• Physics-inspired Biomarker Discovery Toolbox A biomarker identification toolkit that applies computational physics techniques from statistical mechanics to select the most essential predictive variables from high-throughput biomedical datasets for medical diagnosis and prognosis applications, including cancer detection from genomic and proteomic data.
• Map of Science Visualization An interactive network visualization tool that maps the structure of science based on 222 journal subject categories, using force-directed algorithms and the sigma.js framework to render bibliometric networks with community clustering from aggregated journal-journal citation data.
• Network Analysis Services Custom research services covering social network analysis, gene regulatory networks, metadata mapping, bibliometric analysis, and complex systems visualization delivered as consulting engagements for research institutions and pharmaceutical companies.

Quantifiable outcome

• Identified 2-gene predictive signatures that exceed performance of published 8-gene signatures for lung cancer detection from peripheral blood microarray profiling data

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability4 records

Feature3 records

Scale indicators1 record

Recent moves4 records

Expansion highlights3 records

Direct peers

• Genedata: Enterprise bioinformatics platform serving pharma and life sciences with omics data analysis, biomarker discovery workflows, and screening data management — directly comparable on the biomarker discovery use case.
• Schrödinger: Computational drug discovery platform that, like Multi-disciplinary Insights, uses physics-based methods (molecular dynamics, statistical mechanics) combined with machine learning to solve life-sciences problems — the closest analogue in spirit and method.
• Partek (Illumina): Bioinformatics software for genomic and multi-omics data analysis with emphasis on variable selection and statistical modeling — comparable on the high-throughput biological dataset analysis workflow.

Broad incumbents

• Recursion Pharmaceuticals: AI-driven drug discovery company using high-throughput biological data and machine learning — overlaps with Multi-disciplinary Insights' biomarker-discovery and ML-on-biology positioning at much larger scale.
• Clarivate (Web of Science): Operates Web of Science and related scientometrics/bibliometric products built on journal-citation data, directly comparable to Multi-disciplinary Insights' Map of Science visualization on the scientometrics side.
• Elsevier (Scopus / SciVal): Major scientometrics platforms (Scopus, SciVal) providing bibliometric analysis and science mapping — the commercial-scale equivalent of the firm's Map of Science / bibliometric network work.
• QIAGEN Digital Insights: Established bioinformatics software and content portfolio for genomic/clinical interpretation and biomarker analysis — a scaled incumbent in the same target customer base of pharma and biomedical researchers.

Emerging players

• Insitro: Machine-learning-driven drug discovery company combining computational biology with experimental data generation — comparable as a venture-backed ML-for-biology platform pursuing biomarker and target discovery.
• Digital Science (Dimensions): Provider of bibliometric and research-analytics platforms (Dimensions, Altmetric) that map scholarly activity and citation networks — comparable on the science-mapping/scientometrics use case.
• Seven Bridges (Velsera): Cloud-based bioinformatics platform for multi-omics analysis and biomarker workflows, with overlapping use cases in genomic/proteomic variable selection and translational research.

Market position

Strengths4 records

Weaknesses4 records

Competitive moat3 records

Key risks6 records

Key highlights5 records

Customer concentration

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Multi-disciplinary Insights