Uhlolojikelele
I-Insitro ihlanganisa idatha enkulu yofuzo lomuntu kanye neselula ngokufunda komshini ukuze kutholwe okuqondiwe okungcono kwezidakamizwa kanye neziguli okungenzeka kakhulu ziphendule. Ibalulekile ngoba ibhekana nesizathu esikhulu sokuthi izidakamizwa zihluleke - ukukhetha okuqondiwe okungalungile - ngokusungula ukutholwa kubhayoloji yomuntu yangempela.
I-Insitro Machine Learning Biology iqondwa kangcono kumongo wesu, ukufinyelela okuyimodeli, izinqumo zeplathifomu, nobambiswano lwe-ecosystem.
I-Deep Dive
Yasungulwa ngo-2018 isazi sebhayoloji esiyikhompyutha kanye nowayengumholi we-Stanford kanye ne-Coursera u-Daphne Koller, i-Insitro izakhele 'njengenkampani yokufunda umshini kuqala' yokuthola izidakamizwa. Umqondo wayo oyinhloko uwukukhiqiza amasethi edatha amakhulu, akhelwe inhloso endlini - kusetshenziswa amamodeli ezifo asuselwa ku-stem-cell ('in vitro'), izithombe ezinokuqukethwe okuphezulu, kanye nezilinganiso 'ze-omics - futhi zibhangqwe namaqoqo amakhulu abantu ofuzo kanye nomtholampilo njenge-UK Biobank. Ukufunda ngomshini bese kuxhumanisa amangqamuzana namasignesha eselula nesifo, kusize ukukhomba okuhlosiwe ufuzo oluphakamisa ukuthi kubangele ukugula ngempela, futhi kuhlukanise iziguli zibe ngamaqembu amancane. Igama ngokwalo lihlanganisa 'ku-silico' (ikhompuyutha) kanye 'ne-in vitro' (i-lab biology). I-Insitro ibambisene neGileyadi kanye neBristol Myers Squibb futhi igxile ezindaweni ezifana ne-metabolic, isibindi, nezifo ze-neurodegenerative.
I-Technical Insight
Indlela ye-Insitro yesiginesha isebenzisa ukufunda komshini ezithombeni zezokwelapha - isibonelo, amamodeli ajulile afunda i-MRI yesibindi noma i-histopathology - ukuze kutholwe inani 'le-phenotypes lokufunda ngomshini.' Ukuqhuba izifundo zenhlangano yofuzo olubanzi ngokumelene nalezi zici ezitholakala ku-AI kuzo zonke izilinganiso ze-biobank-scale zingaveza okuhlukile kofuzo, ngakho-ke okuhlosiwe okuyimbangela, okugeja amalebula omtholampilo angcolile. Lokhu kuhlanganisa izakhi zofuzo zomuntu, ubufakazi obuqine kakhulu bokuthi okuqondiwe kusemqoka, okunothako lwe-phenotypic resolution oluvela ku-AI.
I-Mastering Insitro Machine Learning Biology
I-Insitro ihlanganisa idatha enkulu yofuzo lomuntu kanye neselula ngokufunda komshini ukuze kutholwe okuqondiwe okungcono kwezidakamizwa kanye neziguli okungenzeka kakhulu ziphendule. Ibalulekile ngoba ibhekana nesizathu esikhulu sokuthi izidakamizwa zihluleke - ukukhetha okuqondiwe okungalungile - ngokusungula ukutholwa kubhayoloji yomuntu yangempela. I-Insitro Machine Learning Biology iqondwa kangcono kumongo wesu, ukufinyelela okuyimodeli, izinqumo zeplathifomu, nobambiswano lwe-ecosystem. Ukuze wakhe ukuqonda okujulile, phatha i-Insitro Machine Learning Biology njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela oyifunayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Insitro Machine Learning Biology ahlola isu lomthengisi, ukwethembeka kwemephu yomgwaqo, kanye nobungozi bokukhiya ngaphambi kokuzibophezela. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ngesikhathi esifanayo, izimemezelo Zokuqalisa zingase zidlule ukuzinza ekusebenzeni kwangempela kokukhiqiza. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Amamodeli okuqeqesha ekuhloleni kwe-MRI yesibindi ukuze akhe ama-phenotypes amaningi, bese eqhuba izifundo zenhlangano yezofuzo ukuze kutholwe okuhlosiwe kwezidakamizwa zesifo sesibindi.
Ukusebenzisa ama-neurons asuselwa ku-stem-cell-derived womuntu ukufanisa i-ALS nezinye izifo ze-neurodeergenerative ekuhlaziyeni i-ML.
Ukusebenzisana neGileyadi ukuthola okuhlosiwe kwe-nonalcoholic steatohepatitis (NASH) kanye ne-liver fibrosis.
Ukuhlukanisa iziguli zibe ngamaqembu amancane ofuzo ukuze kubikezelwe ukuthi ubani ozosabela ekwelashweni okunikeziwe.
Amaphethini Okusebenzisa
I-Insitro Machine Learning Biology in practice
Amamodeli okuqeqesha ekuhloleni kwe-MRI yesibindi ukuze akhe ama-phenotypes amaningi, bese eqhuba izifundo zenhlangano yezofuzo ukuze kutholwe okuhlosiwe kwezidakamizwa zesifo sesibindi.
Amamodeli okuqeqesha ekuhloleni kwe-MRI yesibindi ukuze enze ama-phenotypes amaningi, bese eqhuba izifundo zokuhlanganisa izakhi zofuzo ukuze athole okuhlosiwe kwezidakamizwa zesifo sesibindi Amathimba ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu ngamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Insitro Machine Learning Biology in practice
Ukusebenzisa ama-neurons asuselwa ku-stem-cell-derived womuntu ukufanisa i-ALS nezinye izifo ze-neurodeergenerative ekuhlaziyeni i-ML.
Ukusebenzisa ama-neuron asuselwa ku-stem-cell-cell-derived womuntu ukufanisa i-ALS nezinye izifo ze-neurodegenerative zokuhlaziya i-ML Amathimba ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Insitro Machine Learning Biology in practice
Ukusebenzisana neGileyadi ukuthola okuhlosiwe kwe-nonalcoholic steatohepatitis (NASH) kanye ne-liver fibrosis.
Ukusebenzisana neGileyadi ukuze bathole okuhlosiwe kwe-steatohepatitis (NASH) kanye ne-liver fibrosis Teams ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka kwabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Insitro Machine Learning Biology in practice
Ukuhlukanisa iziguli zibe ngamaqembu amancane ofuzo ukuze kubikezelwe ukuthi ubani ozosabela ekwelashweni okunikeziwe.
Ukuhlanganisa iziguli zibe ngamaqembu amancane ofuzo ukuze zibikezele ukuthi ubani ozosabela ekwelashweni okunikeziwe Amathimba ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yezimo ezibucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Izimemezelo zokwethula zingase zeqe ukuzinza ekugelezeni komsebenzi wangempela wokukhiqiza.
Izintengo ze-API noma izinguquko zenqubomgomo zingaphula ukucabanga ngobusuku obubodwa.
Ukuncika komthengisi oyedwa kukhulisa izindleko zokukhiya nokufuduka.
Ukuqalisa Umhlahlandlela
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha.
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.