목표 화합물과 후보 약물에서 역산
질환 맥락, 기존 데이터, 이미지, 오믹스, 구조 지표를 연결해 탐색할 약효, 독성, 작용 기전 조건을 명확히 합니다.
Integrate Analysis System IAS
IAS는 연구개발 프로세스의 시작부터 끝까지 수작업을 자동화할 수 있습니다.
IAS is not a replacement for every point tool. It is a web-based workflow layer that brings fragmented research data, analysis procedures, review records, and report creation into the same context.
- Absorb format differences
- Standardize analysis steps
- Support collaborative review
Experiment-level integrationConnect imaging, NGS, flow, mass-spec, and molecular structure into one research decision.
IAS는 포인트 툴과 LIMS 사이의 워크플로 레이어입니다
IAS는 모달리티별 전문 도구를 대체하지 않고, 실험 단위 데이터, 분석, 검토, 보고를 하나의 판단 화면으로 연결합니다.
- Connect
어세이 출력을 묶기
이미징, 유세포, 유전체, 질량분석, 분자구조 출력을 실험 단위로 정리합니다.
- Analyze
같은 환경에서 분석
각 모달리티 분석 방법을 같은 워크스페이스에서 다루며 수작업 인계를 줄입니다.
- Link
교차 어세이 근거 연결
여러 어세이 결과를 하나의 연구 판단으로 연결해 비교와 해석을 빠르게 합니다.
- Govern
검토 가능한 출력 생성
분석 이력, 확인, 보고서 작성을 연결해 재현성 있는 최종 산출물을 지원합니다.
탐색 목표를 실험 계획에 되돌려 습식 실험까지 효율화
IAS는 분석 결과 표시를 넘어 목표 화합물, 후보 약물, 가설, 평가 지표, 다음 조건을 정리해 시뮬레이션과 실험 계획에 반영할 수 있게 합니다.
분석과 고찰의 정확도, 보고 효율 향상뿐 아니라 실험 설계의 질을 높이고 후보 탐색부터 습식 검증까지의 연구 사이클을 단축합니다.
시뮬레이션과 실험 설계에 반영
유망 조건, 비교군, 측정 파라미터를 다음 in silico 검토나 습식 실험 계획에 되돌려 시행착오 순서를 정리합니다.
습식 연구의 노력 절감
가능성이 낮은 조건을 일찍 줄이고 필요한 실험, 확인, 검토에 집중해 분석 업무와 습식 실험 양쪽의 공수를 줄입니다.
NEW FEATURE
Now supports ChatGPT and LifeAnalytics' proprietary generative AI app
Helping researchers summarize, interpret, and draft reports from complex analysis results through natural language instructions.
LifeAnalytics IAS now supports ChatGPT and LifeAnalytics' proprietary generative AI application. Using research data, analysis history, experiment records, analysis summaries, and report context, IAS helps researchers summarize, interpret, create reports, and draft scientific manuscripts from complex life science analysis results.
IAS supports multiple analysis areas including image analysis, gene analysis, flow cytometry, mass spectrometry, molecular structure analysis, spatial multi-layer multi-omics analysis, and integrated analysis.
- Summarize and interpret results using natural language
- Organize multiple types of life science analysis data
- Support report creation and manuscript draft preparation
연구개발에서 사용되는 대표 5가지 방법과 통합 분석 구현
IAS는 이미지 분석, 유세포 분석, 차세대 시퀀싱, 분자 구조, 질량분석 등 대표 연구 데이터를 클라우드에서 중앙 관리합니다.
IAS analysis capabilities
Genome Analysis
Visualization, alignment, assembly, and annotation of DNA and protein sequences.
Mass Spectrometry
Acquisition, processing, and analysis of mass spectrometry data, including metabolomics.
Molecular Structure Analysis
High-accuracy prediction of 3D protein structure from amino acid sequences.
Integrated Analysis
AI-assisted discussion reports based on individual analysis results.
Imaging
Image analysis for cells and tissue sections, including 3D, Timelapse, HCS, and pathology.
Flow Cytometry
Flow cytometry analysis such as SPADE, U-MAP, and dot plots.
IAS specification details
IAS Specifications: Analysis Modes, AI Models, and Support Functions
Handle imaging, flow cytometry, genomics, mass spectrometry, molecular structure, and integrated analysis in one research workbench.
IAS is not a single image-analysis application or a collection of isolated point tools. It brings imaging, flow cytometry, genomics, mass spectrometry, molecular structure analysis, integrated analysis, and data management into one research workbench experience.
IAS AI output does not guarantee clinical diagnosis. Interpretation of pathology images, medical images, genomics, molecular structure, and mass-spec results should be combined with specialist review, raw-data inspection, standard methods, and external validation.
Workflow diagramAnalysis mode cards
Each card summarizes inputs, outputs, and review points at a practical evaluation level.
Imaging / DL-ML
- Target data
- Microscopy, pathology, cell, materials, animal time-series, and spatial omics images
- Representative input
- Microscopy, WSI, CT/MRI-like images, timelapse, 3D volume, spatial omics images
- Main output
- Segmentation, ROI analysis, tracking, quantification tables, pathology-support reports
- Review points
- Image quality, ROI settings, mask accuracy, positivity criteria, specialist review
FlowCyto
- Target data
- Flow cytometry and spectral cytometry
- Representative input
- FCS, panel metadata, compensation matrix, gate definitions
- Main output
- Compensation, QC, manual gates, clustering, UMAP/t-SNE, statistics
- Review points
- Compensation, channel names, QC exclusion rules, gate hierarchy, threshold history
Genomics
- Target data
- Sequence analysis, NGS, single-cell, CRISPR, multi-omics
- Representative input
- FASTA, FASTQ, BAM, VCF, H5AD, tabular data
- Main output
- QC, alignment, variants, single-cell clusters, annotations, reports
- Review points
- Genome build, QC, depth, population frequency, reference data, batch correction
MassSpec
- Target data
- MS, MSI, proteomics, metabolomics, lipidomics, DIA, targeted quant
- Representative input
- raw, mzML, mgf, ms2, wiff, peak tables, identification tables, quant tables
- Main output
- Peak detection, identification, quantification, database matching, MSI spatial analysis
- Review points
- QC, blank, batch correction, FDR, standards, retention time, MS/MS evidence
Molecular Structure
- Target data
- Structure prediction, docking, molecular dynamics, function inference
- Representative input
- Sequences, PDB, mmCIF, ligands, complexes, conditions
- Main output
- Predicted and public structures, docking candidates, MD analysis, validation reports
- Review points
- Confidence, pLDDT/PAE, protonation, box, charge, force field, experimental structures
Integrate
- Target data
- Cross-mode integration and decision support
- Representative input
- Mode result tables, HDF5, JSON, CSV, sample IDs
- Main output
- ID mapping, statistical integration, cross-modal relations, Gen Report
- Review points
- Sample ID mapping, missingness, units, batches, correlation versus causation
DB Manage / Sample DB
- Target data
- Data management, metadata, experiment history
- Representative input
- Uploaded files, experiment names, sample tags, analysis history
- Main output
- Input history, file references, mode estimation, re-analysis paths
- Review points
- Sample ID consistency, history, auditability, re-analysis readiness
Analysis-mode details and review points
Long model inventories are grouped by purpose so readers can see how each model is used and what should be checked before interpreting outputs.
Imaging and DL/ML model useMore details
IAS imaging modes cover Tissue, Cell, Material, Semicon, Animal, and ML workflows so teams can choose models by purpose.
Tissue
TissueNet, Foxp3 Spatial, Foxp3 DAB Spatial, Ki67-Br, Pathology, CT/MRI Segment, Spatial Omics, TissueNT2, Thyroid, and Lymph support tissue segmentation, ROI quantification, positivity review, spatial distribution, and multi-channel statistics.
Cell
Cyto, Cyto2, SAM, 2Dtracker, 3Dtimelapse, Nuclei, Nucleus01, Confluency, CM-CellCycle, LiveCell, CP, CPx, TN1-3, LC1-4, Single Cell Protein, 3DVD, LFcell02, and CellPaintingV3 support cell segmentation, tracking, morphology profiling, localization, and state review.
Material / Semicon / Animal / ML
Pore Analysis, Layer, Microridge, Mfiber1, Mfiber2, Wafer, Mouse Dynamics Tracking, and Pixel Classification support pore, layer, ridge, fiber, wafer, animal motion, and user-defined pixel-classification reviews.
Pathology mode: ROI editing, visual similarity, and finding-candidate supportMore details
Pathology mode combines local pathology processing, ROI editing, vision-language assistance, visual similarity, and nuclei/cell segmentation support.
WSI / ROI
OpenSlide, tifffile, OpenCV, and LAB/HSV/RGB Magic Wand support tissue masks, tile heatmaps, overlays, and color-space ROI editing.
AI support
Qwen2.5-VL + LoRA assist, ResNet152 visual similarity, and DeepLIIF biomarker support help generate descriptions, hypotheses, similar-image candidates, and biomarker-support summaries.
Segmentation
checkpoint-gated tumor segmentation, StarDist H&E nuclei, Cellpose-SAM service, and InstanSeg service provide candidate segmentation for tumor regions, nuclei, and cells.
This is not a diagnostic function. Outputs are research and review support for specialist-led pathology review.
FlowCyto: compensation, QC, gates, clustering, and statistics in one flowMore details
IAS connects FCS input, compensation, QC, gates, embedding, clustering, statistics, and reporting.
Compensation / QC
Compensation, spectral unmixing, PeacoQC, DataQC, doublet checks, and margin checks help review spillover, spectral overlap, abnormal events, flow-rate drift, doublets, and margin events.
Gate / Embedding
Gate editor, Boolean gates, UMAP, t-SNE, opt-SNE, and viSNE help preserve manual thresholds and inspect high-dimensional cytometry data.
Cluster / Statistics
FlowSOM, SPADE, CITRUS, population statistics, and marker summaries support population comparison, MFI review, figures, and reports.
Embeddings are visualizations; interpretation should be checked against marker expression and manual gates.
Genomics: sequences, NGS, single-cell, CRISPR, and Multi-OmicsMore details
IAS organizes sequence similarity, alignment, variants, single-cell analysis, reference mapping, and multi-omics evidence by experiment.
Sequence / Variant
BLAST, MSA, Phylogeny, CRISPR, QC & Alignment, Variant Calling, Joint Genotyping, and Annotation connect sequence review to variant and annotation review.
Browser / Single-cell
Genome Browser and Single-cell Async support genomic-region inspection, variants, gene models, normalization, HVG, PCA, clustering, UMAP, and marker detection.
Advanced / Multi-Omics
Harmony, scGPT, cell annotation capability checks, and Multi-Omics connect batch correction, reference mapping, cell-type annotation candidates, expression, variants, phenotypes, and other modes.
Check genome build, QC, depth, population frequency, labeled references, batch effects, missingness, and unit alignment.
MassSpec: peak detection, identification, quantification, MSI, and spectral similarityMore details
IAS connects raw/mzML/mgf/wiff inputs to peak features, identification, quantification, QC, database matching, reports, and multi-omics.
Metabolomics / Lipidomics
MS-DIAL, XCMS, MZmine, MS-FINDER, MS-CleanR, LipidSearch, and LipidBlast outputs are organized for candidate review and quantification.
Proteomics / MSI
MaxQuant, Proteome Discoverer, FragPipe, Mascot, Byonic, Spectronaut, DIA-NN, OpenSWATH, Prosit, SCiLS Lab, Cardinal, MSiReader, METASPACE, and OpenMSI outputs can be reviewed as part of the same workflow.
Target / AI / Multi-omics
MRM/SRM/PRM, MRMProbs, DeepNovo, Spec2Vec, MS2DeepScore, AlphaPept, mixOmics, MOFA(+), DIABLO, and OmicsNet outputs can be tied back to research decisions.
AI candidates should be verified with database matching, standards, MS/MS evidence, FDR, QC, blanks, and batch correction.
Molecular Structure: structure prediction, docking, MD, and validation reportsMore details
IAS helps review the path from Sequence/PDB/Ligand to structure prediction, docking, MD, validation, and reports.
Structure prediction
Monomer and complex structure prediction, public structure retrieval, and pLDDT/PAE review are handled as review inputs.
Docking / MD
GNINA, AutoDock-family, LightDock path, OpenMM, and mdtraj support binding-pose candidates, scores, binding-site review, RMSD/RMSF, interactions, and solvent-condition review.
Validation / Extensions
Validation quality checks, reports, function annotation, spectrum prediction, knowledge graph, QM, and FEP scaffolds help organize quality and extension candidates.
Check confidence, pLDDT/PAE, protonation, box, charge, ligand preparation, force field, solvent, temperature, and simulation length.
Integrate / DB Manage: sample IDs, experiment history, and evidence maturityMore details
IAS connects imaging, genomics, mass spec, flow cytometry, molecular structure, and reports around a consistent sample ID.
Mapping
sample ID and provenance mapping connect sample IDs, experiments, files, and preprocessing history across modes.
Statistics / Cross-modal
compute-statistics, random-effects meta-analysis, and cross-modal review connect effect sizes, confidence intervals, heterogeneity, imaging features, gene expression, metabolites, and population ratios.
Report / DB
Gen Report, report history, evidence maturity, DB Manage, and Sample DB help preserve report context, input history, file references, mode estimation, and re-analysis paths.
For clinical research support, keeping the same sample ID across Imaging, Genomics, MassSpec, and FlowCyto is critical.
Support functions: Support Chat, Patho Chat, and Q&A HelpMore details
Support functions reduce uncertainty during analysis by connecting operation guidance, knowledge search, ROI questions, and help dialogs.
Support Chat
Support Chat connects operation guidance, inquiry handoff, knowledge search, and AI-answer support for Imaging, Genomics, FlowCyto, Molecular, MassSpec, and Integrate.
Patho Chat / ROI Pathology Chat
Patho Chat combines ROI crops, feature extraction, similarity candidates, and Qwen-family LLM support to provide finding candidates, differential candidates, and suggested checks under pathology-specialist review.
Q&A / Help
Q&A icons and Help screens reduce operation mistakes through tooltips, Quick Start, Help dialogs, shortcut explanations, and Support Chat / Patho Chat visibility controls.
Patho Chat does not replace pathology review. Outputs are confirmation support and hypothesis-generation support for selected ROIs.
Five points to check during IAS evaluation
- Input typeWhether images, FCS, FASTQ/BAM/VCF/H5AD, mzML/raw, PDB/mmCIF, or integrated tables fit the target mode.
- Model or engine purposeWhat segmentation, tracking, annotation, clustering, docking, MD, or spectral matching output actually means.
- Output unitWhether the output is ROI, cell, nucleus, population, variant, peptide/protein, metabolite/lipid, structure, or cross-sample statistics.
- Validation itemsWhether QC, thresholds, confidence, FDR, reference databases, external standards, and specialist review remain visible.
- Support functionsWhether Support Chat, Patho Chat, and Q&A / Help give the right workflow guidance in context.
Check IAS fit with your own data
Your target data, analysis problem, and evaluation timing do not need to be fully organized. We can review the appropriate analysis modes and implementation path with you.
Demo consultation기초 연구의 분석 방법을 포괄
기초 연구 분석 방법을 하나의 환경으로 통합해 연구자가 재현성 높은 워크플로로 작업할 수 있게 합니다.
자체 AI와 기능 추가 개발
목표에 맞춘 자체 AI와 분석 기능을 추가 개발해 연구 프로세스 전체 자동화를 지원합니다.
IAS 플랫폼 특징
실험실 전체 연결 및 관리
데이터, 분석, 보고서를 하나의 Web 시스템에서 관리합니다.
완전한 Web 시스템
로컬 설치 없이 협업 및 원격 환경에서도 동일한 분석 플랫폼을 사용합니다.
채팅 기능을 통한 24시간 지원
분석 중 질문과 확인을 원활하게 진행합니다.
견고한 데이터 관리
데이터 관리, 유출 방지, 규제 대응에 적합한 구성입니다.
생성형 AI의 주목 지점 제안
이미지와 분석 결과에서 중요한 포인트 발견을 지원합니다.
400종 이상의 다양한 형식 지원
연구 현장에서 사용하는 다양한 데이터 형식의 통합을 지원합니다.
사용자 의견
분석 업무 표준화
전문 분석 절차를 더 쉽게 공유하여 원격 공동 연구의 재현성을 높입니다.
실험실 전체 효율화
여러 데이터 형식을 중앙에서 관리해 분석과 보고서 작성 시간을 줄입니다.
400종 이상의 다양한 형식 지원
다양한 고유 이미지 형식과 분석 형식을 지원하며 데이터 통합 분석 시스템으로 작동합니다.
지원 데이터 형식 예시
Microscopy and bioimagingRepresentative research image formats from OME-TIFF, confocal, slide scanners, and electron microscopy.
- .ome.tiff
- .ome.tif
- .ome.xml
- .czi
- .lsm
- .lif
- .nd2
- .oib
- .oif
- .oir
- .vsi
- .svs
- .ndpi
- .qptiff
- .dm3
- .dm4
- .mrc
- .mrcs
General image, video, and documentsCommon image, video, tabular, HTML, and XML files used before and after analysis.
- .tif
- .tiff
- .png
- .jpg
- .bmp
- .gif
- .avi
- .mov
- .csv
- .txt
- .xml
- .html
- .h5
- .hdf
NGS and genomicsSequence, alignment, variant, and array-related data organized by experiment.
- fastq
- fasta
- bam
- vcf
- cel
- chp
- arr
- idat
Flow cytometry and qPCRCell population analysis, digital PCR, and instrument outputs with experiment metadata.
- stilla
- rdml
- .pnl
- .rdpnl
- .exp
- .xml
- .csv
Mass spectrometry and chromatographyRaw data, converted data, and vendor formats used around mass spectrometry analysis.
- mzml
- raw
- wiff
- d
- .spc
- .std
- .dat
- .res
Molecular structure, materials, and QCMolecular structure, materials observation, semiconductor, and quality-control inspection outputs.
- .cif
- sdf
- qip
- eds
- lmd
- .sxm
- .afm
- .stp
- .map
- .rec
목록에 없는 형식도 지원 가능하므로 문의해 주세요.
데이터 관리, 유출 방지, 규제와의 높은 적합성
21 CFR Part 11 등 규제 요구사항을 고려한 데이터 관리를 지원합니다.
풍부한 시각화 도구
분석 결과를 직관적으로 확인할 수 있는 시각화 도구를 제공합니다.
보고서 및 논문 초안 작성
분석 결과를 바탕으로 보고서와 원고 초안 작성을 지원합니다.
완전한 실험실 자동화 실현
분석, 관리, 보고를 연결해 실험실 전체 워크플로를 개선합니다.
애플리케이션 예시
Each card separates the input data, the outputs IAS organizes, and the research decision it supports. Flow Cytometry, NGS, Molecular Structure, and MassSpec are treated as first-class analysis modes alongside Imaging.
알려지지 않은 약효 및 독성 유추
신약 개발, 독성, 탐색 연구용
1000개 이상의 측정 파라미터, 10만 개 이상의 세포 수, 세포 소기관 추출 정보를 기반으로 특이 파라미터와 질환 정보를 연결하고 AI가 알려지지 않은 약효와 독성을 유추합니다.
- Inputs
- HCS images, Cell Painting images, tissue or cell images, treatment conditions
- Outputs
- Morphology features, clusters, regions of interest, compound and disease similarity
- Decision use
- Prioritize candidate compounds, flag toxicity signals, and plan follow-up conditions
세포 생산
유전자 치료 및 iPS 세포용
Stem Cell 및 iPS 세포의 연구개발부터 생산, 품질 관리까지 이미지 합격 판정이 필요한 공정을 일관되게 지원합니다.
- Inputs
- FCS files, panel definitions, gating conditions, sample groups
- Outputs
- Population ratios, gate-level comparisons, abnormal population candidates, QC summaries
- Decision use
- Review cell-production lots, compare immune response, and catch quality drift earlier
소동물 동태 추적 및 집단 행동 분석
마우스 등 모델용
주야간이나 체모 차이와 같은 조건에 영향을 받지 않고 소동물의 몸과 사지 동태 추적, 집단 행동 분석을 지원합니다.
- Inputs
- FASTQ/BAM/VCF files, expression tables, sample attributes, phenotype context
- Outputs
- Variant candidates, expression changes, annotations, cross-sample review views
- Decision use
- Shortlist genes, interpret group differences, and choose follow-up validation targets
Molecular Structure: 3D structure comparison
Proteins, materials, and structural evaluation
Manage 3D structures, segmented regions, features, and candidate models so structural differences and regions of interest are easier to compare.
- Inputs
- PDB/mmCIF files, predicted structures, binding sites, candidate molecules or materials
- Outputs
- Structure overlays, difference regions, binding-site notes, comparison reports
- Decision use
- Review structure hypotheses, compare candidates, and prepare docking or materials evaluation
MassSpec: Spectral peak interpretation
Metabolites, quality evaluation, and component comparison
Manage spectra, peaks, sample conditions, and comparison groups to support component review, quality differences, and reporting.
- Inputs
- mzML/mzXML files, peak lists, sample conditions, comparison groups or lots
- Outputs
- Peak candidates, component comparisons, quality differences, report annotations
- Decision use
- Confirm metabolite candidates, understand lot differences, and organize quality evidence