INSILICOM LLC - Florida Company Profile

702F0

2020-09-30

www.insilicom.com

2019-10-01

2013-10-23

702F0

Active

Non-Manufacturer

2025-02-04

2030-02-04

2026-02-03

JINFENG ZHANG

www.insilicom.com

75N93024C00034

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AWARD

DEFINITIVE CONTRACT

300000.00

300000.00

300000.00

Department of Health and Human Services

2024-09-05

SBIR TOPIC 136 - INSILICOM

541715: RESEARCH AND DEVELOPMENT IN THE PHYSICAL, ENGINEERING, AND LIFE SCIENCES (EXCEPT NANOTECHNOLOGY AND BIOTECHNOLOGY)

AN11: HEALTH R&D SERVICES; HEALTH CARE SERVICES; BASIC RESEARCH

75N91024C00007

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AWARD

DEFINITIVE CONTRACT

355000.00

355000.00

355000.00

Department of Health and Human Services

2024-02-15

SBIR FY24 CONCEPT AWARD-DRUG REPURPOSING FOR RARE CANCERS USING A LARGE-SCALE, HIGH-QUALITY BIOMEDICAL KNOWLEDGE GRAPH

541715: RESEARCH AND DEVELOPMENT IN THE PHYSICAL, ENGINEERING, AND LIFE SCIENCES (EXCEPT NANOTECHNOLOGY AND BIOTECHNOLOGY)

AN12: HEALTH R&D SERVICES; HEALTH CARE SERVICES; APPLIED RESEARCH

2024-07-17

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National Science Foundation

APTO: CONSTRUCTING CAUSAL KNOWLEDGE GRAPHS FOR ASSESSING AND PREDICTING TECHNOLOGY OUTCOMES -ASSESSING AND PREDICTING TECHNOLOGY OUTCOMES (APTO) IS CRUCIAL FOR EVALUATING THE IMPACT OF R&D INVESTMENTS ON INNOVATION, ECONOMIC GROWTH, AND NATIONAL COMPETITIVENESS. TACKLING THIS COMPLEX TASK REQUIRES APPROPRIATE DATASETS AND EFFECTIVE DATA CREATION TOOLS. THE LATEST NATURAL LANGUAGE PROCESSING (NLP) TECHNOLOGIES HAVE REACHED HUMAN-LEVEL PERFORMANCE IN CERTAIN CRUCIAL INFORMATION EXTRACTION TASKS, AS EVIDENCED BY RESULTS FROM COMMUNITY-ORGANIZED CHALLENGES. THIS PROJECT WILL LEVERAGE AN AWARD-WINNING PIPELINE FOR CONSTRUCTING KNOWLEDGE GRAPHS (KGS) BY EXPANDING IT SUBSTANTIALLY TO INCLUDE A DIVERSE SET OF TECHNOLOGY RELATED ENTITIES AND THEIR RELATIONS. KGS COMPRISE ENTITIES LIKE DISEASES, GENES, DRUGS, ETC., AND THEIR RELATIONS, INCLUDING ASSOCIATIONS, BINDINGS, POSITIVE CORRELATIONS, ETC. THE ENHANCED KG WILL BE READY FOR DEVELOPING MODELS FOR PREDICTING TECHNOLOGY OUTCOMES IN HEALTHCARE AND DRUG DISCOVERY. IN ADDITION TO THE DATASET, THE PROJECT TEAM WILL ALSO DEVELOP AN END-TO-END TOOLKIT FOR FELLOW APTO TEAMS TO PERFORM INFORMATION EXTRACTION TASKS AND CONSTRUCT KGS IN THEIR OWN DOMAINS. BY UTILIZING THE LATEST ADVANCEMENTS IN NLP AND PREDICTIVE MODELING, THE PROJECT WILL PROVIDE A COMPREHENSIVE ASSESSMENT OF THE CAPABILITIES AND APPLICATIONS OF BIOMEDICAL TECHNOLOGIES. THIS WILL NOT ONLY INFORM R&D INVESTMENTS BUT WILL ALSO CONTRIBUTE TO INFORMED DECISION-MAKING IN HEALTHCARE AND TECHNOLOGY POLICY, AS WELL AS ADDRESS THE DISPARITIES BETWEEN HEALTHCARE SPENDING AND OUTCOMES. FURTHERMORE, THE PROJECT'S APPROACH OF EXTRACTING VAST AMOUNTS OF INFORMATION FROM TEXT TO BUILD PREDICTIVE MODELS CAN BE APPLIED TO OTHER SECTORS, ADVANCING RESEARCH AND KNOWLEDGE ACROSS VARIOUS FIELDS. ULTIMATELY, THIS PROJECT HAS THE POTENTIAL TO DRIVE STRATEGIC INVESTMENTS IN TECHNOLOGY AND INNOVATION, IMPROVING HEALTH OUTCOMES AND FOSTERING ECONOMIC PROSPERITY ON A NATIONAL AND GLOBAL SCALE. THIS PROJECT WILL LEVERAGE A PIPELINE RECENTLY DEVELOPED THAT WON THE NIH-ORGANIZED LITCOIN NLP CHALLENGE, A COMPETITION THAT EVALUATED METHODS FOR CONSTRUCTING BIOMEDICAL KNOWLEDGE GRAPHS (KGS) BY EXTRACTING ENTITIES AND THEIR RELATIONS FROM BIOMEDICAL TEXTS. USING THIS PIPELINE, THE PROJECT TEAM CREATED A LARGE-SCALE KG BY EXTRACTING INFORMATION FROM ALL PUBMED ABSTRACTS. THE KG, NAMED IKRAPH, CONTAINS SUBSTANTIALLY MORE INFORMATION THAN THAT IN PUBLIC DATABASES. TO ADAPT IKRAPH FOR CAUSAL INFERENCE, THE PROJECT TEAM ANNOTATED DIRECTION INFORMATION FOR THE RELATIONS IN THE LITCOIN DATASET AND DEVELOPED MODELS TO PREDICT THE DIRECTION OF RELATIONS, WHICH ENABLED THE CONSTRUCTION OF A CAUSAL KG CAPABLE OF INFERRING CAUSALITY BETWEEN INDIRECTLY CONNECTED ENTITIES. IN THIS PROJECT, IKRAPH WILL BE ENHANCED BY ADDING A DIVERSE SET OF TECHNOLOGY RELATED ENTITIES AND THEIR RELATIONS SUCH AS EQUIPMENT, TECHNOLOGY, TECHNOLOGY FEATURES, FEATURE VALUES, PROBLEMS, METHODS, DATA TYPES, DATASETS, AND GEOGRAPHICAL ENTITIES ETC. THE PROJECT TEAM WILL EXTRACT RELEVANT INFORMATION FROM UNSTRUCTURED TEXT INCLUDING PUBMED ABSTRACTS, PUBMED CENTRAL FULL-TEXT ARTICLES, PATENTS, MARKETING REPORTS, AND WIKIPEDIA ARTICLES. RELEVANT DATA FROM PUBLIC DATABASES WILL ALSO BE INTEGRATED INTO IKRAPH. THE TOOLKIT FOR CONSTRUCTING KGS, DESIGNED FOR END-TO-END ANNOTATION AND MODEL BUILDING, WILL UTILIZE AN AI-ASSISTED METHODOLOGY. THIS APPROACH INCORPORATES AI MODELS AT EVERY STAGE OF THE ANNOTATION PROCESS TO ENHANCE QUALITY AND SIGNIFICANTLY IMPROVE EFFICIENCY. FINALLY, THE PROJECT TEAM WILL CONDUCT A CASE STUDY ON ADVANCED MANUFACTURING TECHNOLOGIES (AMT) FOR THE PRODUCTION OF GENERIC OFF-PATENT DRUGS USING THE CONSTRUCTED KG. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

1932818.00

0.00

0.00

2024-02-14

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National Science Foundation

SBIR PHASE I: KNOWLEDGE GRAPH-POWERED INFORMATION RETRIEVAL AND CAUSAL INFERENCE -THE BROADER/COMMERCIAL IMPACT OF THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE I PROJECT IS AS FOLLOWS. THE EXPONENTIAL GROWTH OF SCIENTIFIC LITERATURE POSES TWO CRITICAL CHALLENGES: (1) MISSING IMPORTANT PRIOR STUDIES DURING RESEARCH DESIGN CAN LEAD TO RESOURCE AND TIME WASTAGE, INCORRECT CONCLUSIONS, AND MISSED DISCOVERIES, AND (2) EFFECTIVELY UTILIZING THE VAST VOLUME OF SCIENTIFIC KNOWLEDGE IN RAW TEXT FORM HAS BECOME INCREASINGLY DIFFICULT. THIS PROJECT AIMS TO BUILD DISRUPTIVE, COMMERCIALLY VALUABLE PRODUCTS THAT ADDRESS THESE CHALLENGES, BENEFITING THE PHARMACEUTICAL INDUSTRY AND ACADEMIC RESEARCH. IN ADDITION, THE SUCCESS OF THE PROJECT IN DEVELOPING ADVANCED AI TECHNOLOGIES WILL HAVE A SIGNIFICANT IMPACT ON THE GROWTH AND DEVELOPMENT OF THE AI INDUSTRY IN TALLAHASSEE, FL, AND THE BROADER SOUTHEAST REGION OF THE UNITED STATES. THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE I PROJECT AIMS TO DEVELOP AI-POWERED, COMMERCIALLY VIABLE APPLICATIONS ENABLED BY A LARGE-SCALE BIOMEDICAL KNOWLEDGE GRAPH (KG) CONSTRUCTED RECENTLY USING AN AWARD-WINNING NATURAL LANGUAGE PROCESSING (NLP) PIPELINE. THE KG HAS BEEN FURTHER TRANSFORMED INTO A CAUSAL KG BY INTEGRATING CAUSAL RELATIONS AND ENHANCED BY INCORPORATING DATA FROM 40 PUBLIC DATABASES AND ANALYSIS RESULTS OF SOME COMMONLY USED GENOMICS DATASETS. TO FACILITATE SEAMLESS ACCESS TO THE KG, THE PROJECT TEAM HAS DEVELOPED A VERSATILE QUERY INTERFACE NAMED IEXPLORE. THIS INTERFACE ENABLES HIGHLY ACCURATE INFORMATION RETRIEVAL AND SUPPORTS CAUSAL INFERENCE, PROVIDING USERS WITH VALUABLE INSIGHTS. IN THE CURRENT PROJECT, INSILICOM LLC WILL FURTHER INCREASE THE COVERAGE OF THE KG AND BUILD A NOVEL LITERATURE ALERT SYSTEM CALLED IPULSE. BY COMBINING THE ADVANCEMENTS IN AI, THE RICHNESS OF THE KNOWLEDGE GRAPH, AND THE UTILITY OF THE QUERY INTERFACE AND LITERATURE ALERT SYSTEM, THIS PROJECT WILL RESULT IN PRACTICAL AND COMMERCIALLY VIABLE APPLICATIONS THAT WILL REVOLUTIONIZE THE WAY BIOMEDICAL KNOWLEDGE IS ACCESSED, INTERPRETED, AND UTILIZED. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

275000.00

0.00

0.00

2023-08-16

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Department of Health and Human Services

CONSTRUCTING A LARGE-SCALE BIOMEDICAL KNOWLEDGE GRAPH USING ALL PUBMED ABSTRACTS AND PMC FULL-TEXT ARTICLES AND ITS APPLICATIONS - PROJECT SUMMARY THE NUMBER OF BIOMEDICAL PUBLICATIONS IS GROWING AT AN ACCELERATED SPEED. THIS EVER-INCREASING AMOUNT OF SCIENTIFIC LITERATURE HAS MADE READING ALL THE PUBLISHED ARTICLES REGULARLY IMPOSSIBLE EVEN FOR A VERY SPECIFIC RESEARCH AREA. THE LARGE VOLUMES OF SCIENTIFIC PUBLICATION HAVE ALSO MADE IT VERY CHALLENGING FOR MODERN SEARCH ENGINES TO FIND RELEVANT ARTICLES ACCURATELY FOR A GIVEN QUERY. MISSING IMPORTANT PRIOR STUDIES IN LITERATURE SEARCH CAN HAVE SERIOUS CONSEQUENCES SUCH AS WASTING RESOURCES/TIME AND/OR MAKING WRONG SCIENTIFIC CONCLUSIONS. ANOTHER UNMET CHALLENGE IN LITERATURE SEARCH IS THAT RESEARCHERS OFTEN PREFER FINDING ARTICLES WHERE THE QUERIES THEY USE ARE PART OF THE NEW DISCOVERIES, INSTEAD OF THE BACKGROUND KNOWLEDGE IN THE ARTICLES. THE CURRENT SEARCH ENGINES CANNOT DISTINGUISH BETWEEN NEW DISCOVERIES AND BACKGROUND KNOWLEDGE IN AN ARTICLE. RELATED TO THIS CHALLENGE IS THAT IT CAN BE DIFFICULT TO IDENTIFY THE LATEST DISCOVERIES IN A PARTICULAR SCIENTIFIC AREA WITHOUT READING ALL THE RECENTLY PUBLISHED ARTICLES. TO ADDRESS THESE CHALLENGES, ONE CAN CONVERT UNSTRUCTURED TEXT DATA INTO STRUCTURED FORM, WHICH CAN THEN SUPPORT HIGHLY ACCURATE INFORMATION RETRIEVAL, INFORMATION INTEGRATION AND AUTOMATED KNOWLEDGE DISCOVERY. A PLAUSIBLE APPROACH FOR CONVERTING UNSTRUCTURED TEXT INTO STRUCTURED FORM IS TO USE NAMED ENTITY RECOGNITION (NER) AND RELATION EXTRACTION (RE) METHODS TO IDENTIFY THE BIOLOGICAL ENTITIES AND EXTRACT THEIR RELATIONS TO CONSTRUCT KNOWLEDGE GRAPHS (KGS). KGS CAN LINK CONCEPTS WITHIN EXISTING RESEARCH TO ALLOW RESEARCHERS TO FIND CONNECTIONS THAT MAY HAVE BEEN DIFFICULT TO DISCOVER WITHOUT THEM. THE LITCOIN NATURAL LANGUAGE PROCESSING (NLP) CHALLENGE WAS RECENTLY ORGANIZED BY NCATS OF NIH AND NASA TO SPUR INNOVATION BY REWARDING THE MOST CREATIVE AND HIGH-IMPACT USES OF BIOMEDICAL, PUBLICATION-FREE TEXT TO CREATE KGS. IN ADDITION TO ENTITIES AND RELATIONS, THE MANUALLY ANNOTATED DATASET PROVIDED BY LITCOIN ALSO CONTAINS THE ANNOTATIONS OF RELATIONS BEING NEW DISCOVERIES OR BACKGROUND KNOWLEDGE. OUR TEAM HAS PARTICIPATED IN THE CHALLENGE AND RANKED THE FIRST PLACE. THIS APPLICATION AIMS TO APPLY THE METHODS WE HAVE DEVELOPED FOR LITCOIN TO ALL PUBMED ABSTRACTS AND PMC FULL-TEXT ARTICLES TO BUILD THE LARGEST SCALE KG TO DATE AND DEVELOP APPLICATIONS ON TOP OF IT. SPECIFICALLY, WE WILL (1) DEVELOP A KNOWLEDGE VISUALIZATION AND NAVIGATION TOOL COMBINED WITH A DEEP LEARNING-POWERED SEARCH ENGINE WE DEVELOPED PREVIOUSLY; (2) DEVELOP ADVANCED RELATION SEARCH FUNCTIONS TO ALLOW KNOWLEDGE DISCOVERY APPLICATIONS SUCH AS DRUG REPURPOSING AND ADVERSE EFFECT DISCOVERY; (3) DEVELOP FUNCTIONS THAT ALLOW USERS TO SEARCH SPECIFICALLY THE NEW DISCOVERIES IN ARTICLES; AND (4) DEVELOP FUNCTIONS THAT RETURN THE LATEST DISCOVERIES IN A SCIENTIFIC AREA FOR A GIVEN TIME PERIOD.

286324.00

0.00

0.00

2021-01-27

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Small Business Administration

TO AID SMALL BUSINESSES IN MAINTAINING WORK FORCE DURING COVID-19 PANDEMIC.

0.00

26432.00

26432.00

2020-05-03

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Small Business Administration

TO AID SMALL BUSINESSES IN MAINTAINING WORK FORCE DURING COVID-19 PANDEMIC.

0.00

26430.00

26430.00

2

$26,432

2021-01-27

Paid in Full

100

$26,432

Unanswered

Unknown/NotStated

Unanswered

Unanswered

$26,594.94

Bank of America, National Association

Payroll: $26,430

2

$26,430

2020-05-01

Paid in Full

100

$26,430

Unanswered

Unknown/NotStated

Unanswered

Unanswered

$26,613.92

Bank of America, National Association

Payroll: $26,430