Folding project #14937, CANCER research

Research section Research projects and references

Project #14937 overview

Project Summary AI Beta

Class F Receptors help control how cells develop. When these receptors are overactive, they can contribute to cancers like Basal Cell Carcinoma and Medulloblastoma. This project uses computer simulations to understand how these receptors work, hoping to find new ways to treat these diseases.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Prateek Bansal
Institution: University of Illinois Urbana-Champaign

Work unit

Atoms: 105,048
Core: OPENMM_22
Status: Public

Source material

Official Project Description

Class F Receptors Class F Receptors are involved in the control of cell differentiation.

Over-activation of these proteins has links to Basal Cell Carcinoma and Medulloblastoma.

Through simulations we aim to understand the activation mechanisms of these proteins, giving us a way to probe into the pathogenesis of the disease.

Reading reference

Related Terms Glossary AI Beta

Automated high-level definitions; simplified and may not be fully accurate.

Class F Receptors

A family of G protein-coupled receptors involved in cell signaling and differentiation.

Scientific: BiotechnologyCellular Biology / Signal Transduction

Class F Receptors are a group of proteins found on the surface of cells. They act as receptors, receiving signals from other molecules and triggering responses within the cell. These receptors are important for processes like cell growth, development, and survival. Disruptions in their function can contribute to diseases like Basal Cell Carcinoma and Medulloblastoma.

Basal Cell Carcinoma

A type of skin cancer that originates in the basal cells.

Pathological: HealthcareOncology / Skin Cancer

Basal Cell Carcinoma is the most common type of skin cancer. It develops from the basal cells, which are found in the deepest layer of the epidermis (outer skin layer). This cancer often appears as a pearly or waxy bump and can be slow-growing.

Medulloblastoma

A type of malignant brain tumor that arises from the cerebellum.

Pathological: HealthcareOncology / Brain Tumors

Medulloblastoma is a serious type of cancer that begins in the cerebellum, a part of the brain responsible for balance and coordination. It's most common in children.

GPU PPD Averages

Rank Project	Model Name Folding@Home Identifier	Make Brand	GPU Model	PPD Average	Points WU Average	WUs Day Average	WU Time Average
1	GeForce RTX 3090 Ti GA102 [GeForce RTX 3090 Ti]	Nvidia	GA102	8,057,773	146,663	54.94	0 ^hrs 26 ^mins
2	GeForce RTX 3090 GA102 [GeForce RTX 3090]	Nvidia	GA102	7,068,101	140,675	50.24	0 ^hrs 29 ^mins
3	GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti]	Nvidia	GA102	6,617,547	135,275	48.92	0 ^hrs 29 ^mins
4	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	6,530,645	138,362	47.20	0 ^hrs 31 ^mins
5	GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate]	Nvidia	GA102	6,100,992	132,742	45.96	0 ^hrs 31 ^mins
6	GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448	Nvidia	TU102	4,959,468	125,888	39.40	0 ^hrs 37 ^mins
7	GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448	Nvidia	TU102	4,701,842	124,072	37.90	0 ^hrs 38 ^mins
8	RTX A5000 GA102GL [RTX A5000]	Nvidia	GA102GL	4,602,656	122,909	37.45	0 ^hrs 38 ^mins
9	GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate]	Nvidia	GA104	4,567,680	122,407	37.32	0 ^hrs 39 ^mins
10	GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti]	Nvidia	GA104	4,193,128	118,089	35.51	0 ^hrs 41 ^mins
11	GeForce RTX 3070 GA104 [GeForce RTX 3070]	Nvidia	GA104	4,036,920	117,912	34.24	0 ^hrs 42 ^mins
12	GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate]	Nvidia	GA104	3,258,635	108,653	29.99	0 ^hrs 48 ^mins
13	GeForce RTX 2080 Super TU104 [GeForce RTX 2080 SUPER]	Nvidia	TU104	3,234,700	110,025	29.40	0 ^hrs 49 ^mins
14	GeForce RTX 3070 Mobile / Max-Q GA104M [GeForce RTX 3070 Mobile / Max-Q]	Nvidia	GA104M	3,227,085	109,974	29.34	0 ^hrs 49 ^mins
15	GeForce RTX 3080 Mobile / Max-Q 8GB/16GB GA104M [GeForce RTX 3080 Mobile / Max-Q 8GB/16GB]	Nvidia	GA104M	3,161,178	110,324	28.65	0 ^hrs 50 ^mins
16	GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218	Nvidia	TU104	3,154,888	107,622	29.31	0 ^hrs 49 ^mins
17	Quadro RTX 6000/8000 TU102GL [Quadro RTX 6000/8000]	Nvidia	TU102GL	3,088,792	107,188	28.82	0 ^hrs 50 ^mins
18	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,335,850	98,460	23.72	1 ^hrs 1 ^mins
19	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER]	Nvidia	TU106	2,237,071	97,027	23.06	1 ^hrs 2 ^mins

20	GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate]	Nvidia	GA106	2,149,249	95,395	22.53	1 ^hrs 4 ^mins
21	GeForce RTX 3060 GA104 [GeForce RTX 3060]	Nvidia	GA104	2,028,901	94,240	21.53	1 ^hrs 7 ^mins
22	Quadro RTX 4000 TU104GL [Quadro RTX 4000]	Nvidia	TU104GL	1,896,218	91,697	20.68	1 ^hrs 10 ^mins
23	GeForce RTX 2080 TU104 [GeForce RTX 2080]	Nvidia	TU104	1,839,129	92,483	19.89	1 ^hrs 12 ^mins
24	GeForce RTX 2070 TU106 [GeForce RTX 2070]	Nvidia	TU106	1,690,483	88,215	19.16	1 ^hrs 15 ^mins
25	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	1,482,950	83,272	17.81	1 ^hrs 21 ^mins
26	Geforce RTX 3050 GA106 [Geforce RTX 3050]	Nvidia	GA106	1,309,408	80,669	16.23	1 ^hrs 29 ^mins
27	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,302,815	78,555	16.58	1 ^hrs 27 ^mins
28	GeForce RTX 2060 Mobile / Max-Q TU106M [GeForce RTX 2060 Mobile / Max-Q]	Nvidia	TU106M	1,238,620	79,270	15.63	1 ^hrs 32 ^mins
29	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,236,458	80,053	15.45	1 ^hrs 33 ^mins
30	GeForce RTX 2060 TU104 [GeForce RTX 2060]	Nvidia	TU104	1,221,028	79,446	15.37	1 ^hrs 34 ^mins
31	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	1,218,741	78,288	15.57	1 ^hrs 33 ^mins
32	GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile]	Nvidia	TU116M	1,180,386	78,499	15.04	1 ^hrs 36 ^mins
33	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,091,997	76,167	14.34	1 ^hrs 40 ^mins
34	GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186	Nvidia	GP104	924,575	72,049	12.83	1 ^hrs 52 ^mins
35	Tesla M40 GM200GL [Tesla M40] 6844	Nvidia	GM200GL	907,425	71,745	12.65	1 ^hrs 54 ^mins
36	GeForce GTX 1660 TU116 [GeForce GTX 1660]	Nvidia	TU116	781,693	68,800	11.36	2 ^hrs 7 ^mins
37	P104-100 GP104 [P104-100]	Nvidia	GP104	771,235	68,739	11.22	2 ^hrs 8 ^mins
38	GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372	Nvidia	GP106	723,502	66,702	10.85	2 ^hrs 13 ^mins
39	GeForce GTX 1650 TU117 [GeForce GTX 1650]	Nvidia	TU117	520,443	59,777	8.71	2 ^hrs 45 ^mins

40	GeForce GTX 980 GM204 [GeForce GTX 980] 4612	Nvidia	GM204	491,611	58,668	8.38	2 ^hrs 52 ^mins
41	Quadro P2200 GP106GL [Quadro P2200]	Nvidia	GP106GL	473,506	59,941	7.90	3 ^hrs 2 ^mins
42	P106-090 GP106 [P106-090]	Nvidia	GP106	310,421	50,610	6.13	3 ^hrs 55 ^mins
43	GeForce GTX 1050 Ti Mobile GP107M [GeForce GTX 1050 Ti Mobile]	Nvidia	GP107M	298,541	49,337	6.05	3 ^hrs 58 ^mins
44	GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q]	Nvidia	TU117M	292,820	46,639	6.28	3 ^hrs 49 ^mins
45	GeForce GTX 950 GM206 [GeForce GTX 950] 1572	Nvidia	GM206	226,935	45,280	5.01	4 ^hrs 47 ^mins
46	GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935	Nvidia	GP106	222,657	26,270	8.48	2 ^hrs 50 ^mins
47	GeForce GTX 750 Ti GM107 [GeForce GTX 750 Ti] 1389	Nvidia	GM107	128,143	37,550	3.41	7 ^hrs 2 ^mins
48	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	121,809	27,190	4.48	5 ^hrs 21 ^mins
49	GeForce GTX 660 Ti GK104 [GeForce GTX 660 Ti] 2634	Nvidia	GK104	111,959	36,155	3.10	7 ^hrs 45 ^mins
50	GeForce GT 1030 GP108 [GeForce GT 1030] 1127	Nvidia	GP108	101,878	34,552	2.95	8 ^hrs 8 ^mins
51	Quadro K2200 GM107GL [Quadro K2200]	Nvidia	GM107GL	82,479	33,959	2.43	9 ^hrs 53 ^mins
52	Quadro P620 GP107GL [Quadro P620]	Nvidia	GP107GL	69,566	30,698	2.27	10 ^hrs 35 ^mins
53	Quadro K1200 GM107GL [Quadro K1200]	Nvidia	GM107GL	50,902	27,640	1.84	13 ^hrs 2 ^mins

Research: CANCER Folding Project #14937

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 14937

GPU PPD Averages

Research: CANCER Folding Project #14937

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages